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hgkim 6 months ago
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248
.gitignore vendored
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# ---> Python
# Byte-compiled / optimized / DLL files
# -----------------------------
# OS 기본 파일
# -----------------------------
.DS_Store
Thumbs.db
# -----------------------------
# Python 환경
# -----------------------------
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.pyc
*.pyo
*.pyd
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
# 가상환경
env/
venv/
.mipenv
/*.egg-info/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Poetry / pipenv
.cache/
.venv/
# -----------------------------
# IDE / Editor 관련
# -----------------------------
.vscode/
.idea/
*.swp
*.swo
# -----------------------------
# 데이터, 모델, 체크포인트
# -----------------------------
# 모델 파일
*.pt
*.pth
*.ckpt
*.onnx
*.trt
*.pb
*.h5
# 학습 관련 출력
runs/
logs/
tensorboard/
lightning_logs/
checkpoint/
checkpoints/
# 데이터셋 (원하면 제외 가능)
data/
dataset/
datasets/
# 결과물 (이미지/비디오/추론 결과)
outputs/
results/
inference/
*.jpg
*.png
*.jpeg
*.bmp
*.mp4
*.avi
# -----------------------------
# PyTorch & HuggingFace 캐시
# -----------------------------
/root/.cache/torch/
/cache/
/.torch/
huggingface/
transformers/
# -----------------------------
# Jupyter 관련
# -----------------------------
.ipynb_checkpoints/
*.ipynb~
# -----------------------------
# 컴파일/빌드 아티팩트
# -----------------------------
build/
dist/
*.bin
# Django stuff:
# -----------------------------
# Temp 파일
# -----------------------------
tmp/
temp/
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
# -----------------------------
# 환경 변수 파일
# -----------------------------
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
.env.*

1
Paddle_ocr/PaddleOCR

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Subproject commit 8853b21cfea6863c9a6797f7cb8cf0da8c50d920

126
Paddle_ocr/inference_pgnet.py
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import argparse
import os
import cv2
import numpy as np
import onnxruntime
from paddleocr.ppocr.data.imaug.operators import (E2EResizeForTest, KeepKeys,
NormalizeImage, ToCHWImage)
from paddleocr.ppocr.postprocess.pg_postprocess import PGPostProcess
# from pgnet.chr_dct import chr_dct_list
class PGNetPredictor:
def __init__(self, img_path, cpu):
self.img_path = img_path
self.dict_path = "ic15_dict.txt"
# if not os.path.exists(self.dict_path):
# with open(self.dict_path, "w") as f:
# f.writelines(chr_dct_list)
if not cpu:
providers = ["CUDAExecutionProvider"]
else:
providers = ["CPUExecutionProvider"]
self.sess = onnxruntime.InferenceSession(
args.model_path, providers=providers)
def preprocess(self, img_path):
img = cv2.imread(img_path)
self.ori_im = img.copy()
data = {"image": img}
transforms = [
E2EResizeForTest(max_side_len=768, valid_set="totaltext"),
NormalizeImage(
scale=1.0 / 255.0,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
order="hwc",
),
ToCHWImage(),
KeepKeys(keep_keys=["image", "shape"]),
]
for transform in transforms:
data = transform(data)
img, shape_list = data
img = np.expand_dims(img, axis=0)
shape_list = np.expand_dims(shape_list, axis=0)
return img, shape_list
def predict(self, img):
ort_inputs = {self.sess.get_inputs()[0].name: img}
outputs = self.sess.run(None, ort_inputs)
preds = {}
preds["f_border"] = outputs[0]
preds["f_char"] = outputs[1]
preds["f_direction"] = outputs[2]
preds["f_score"] = outputs[3]
return preds
def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
img_height, img_width = image_shape[0:2]
dt_boxes_new = []
for box in dt_boxes:
box = self.clip_det_res(box, img_height, img_width)
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
def clip_det_res(self, points, img_height, img_width):
for pno in range(points.shape[0]):
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
return points
def postprocess(self, preds, shape_list):
pgpostprocess = PGPostProcess(
character_dict_path=self.dict_path,
valid_set="totaltext",
score_thresh=0.5,
mode="fast",
)
post_result = pgpostprocess(preds, shape_list)
points, strs = post_result["points"], post_result["texts"]
dt_boxes = self.filter_tag_det_res_only_clip(points, self.ori_im.shape)
return dt_boxes, strs
def __call__(self):
img, shape_list = self.preprocess(self.img_path)
preds = self.predict(img)
dt_boxes, strs = self.postprocess(preds, shape_list)
return dt_boxes, strs
def draw(self, dt_boxes, strs, img_path):
src_im = cv2.imread(img_path)
width, height, _ = src_im.shape
for box, str in zip(dt_boxes, strs):
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(
255, 255, 0), thickness=2)
cv2.putText(
src_im,
str,
org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
fontFace=cv2.FONT_HERSHEY_COMPLEX,
fontScale=0.7 / 500 * width / 2,
color=(0, 255, 0),
thickness=int(1 / 1000 * width),
)
img_out_name = os.path.basename(img_path).split(".")[0]
img_out_name = f"{img_out_name}_pgnet.jpg"
cv2.imwrite(img_out_name, src_im)
return src_im
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PGPNET inference")
parser.add_argument("model_path", type=str, help="onnxmodel path")
parser.add_argument("img_path", type=str, help="image path")
parser.add_argument(
"--cpu", action="store_true", help="cpu inference, default device is gpu"
)
args = parser.parse_args()
pgnetpredictor = PGNetPredictor(args.img_path, args.cpu)
dt_boxes, strs = pgnetpredictor()
print(f"Predict string:{strs}")
pgnetpredictor.draw(dt_boxes, strs, args.img_path)

2
Paddle_ocr/requirements.txt
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onnxruntime-gpu
paddleocr

127
Paddle_ocr/video_pgnet_infer.py
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import argparse
import os
import cv2
import numpy as np
import onnxruntime
from paddleocr.ppocr.data.imaug.operators import (
E2EResizeForTest, KeepKeys, NormalizeImage, ToCHWImage
)
# from ppocr.data.imaug.operators import (
# E2EResizeForTest, KeepKeys, NormalizeImage, ToCHWImage
# )
from paddleocr.ppocr.postprocess.pg_postprocess import PGPostProcess
# from ppocr.postprocess.pg_postprocess import PGPostProcess
from pgnet.chr_dct import chr_dct_list
class PGNetPredictor:
def __init__(self, model_path, cpu=False):
self.model_path = model_path
self.dict_path = "ic15_dict.txt"
if not os.path.exists(self.dict_path):
with open(self.dict_path, "w") as f:
f.writelines(chr_dct_list)
providers = ["CPUExecutionProvider"] if cpu else ["CUDAExecutionProvider"]
self.sess = onnxruntime.InferenceSession(model_path, providers=providers)
self.transforms = [
E2EResizeForTest(max_side_len=768, valid_set="totaltext"),
NormalizeImage(scale=1/255.0, mean=[0.485,0.456,0.406],
std=[0.229,0.224,0.225], order="hwc"),
ToCHWImage(),
KeepKeys(keep_keys=["image", "shape"]),
]
self.pgpostprocess = PGPostProcess(
character_dict_path=self.dict_path,
valid_set="totaltext",
score_thresh=0.5,
mode="fast",
)
def preprocess(self, img):
self.ori_im = img.copy()
data = {"image": img}
for transform in self.transforms:
data = transform(data)
img, shape_list = data
return np.expand_dims(img, axis=0), np.expand_dims(shape_list, axis=0)
def predict(self, img):
ort_inputs = {self.sess.get_inputs()[0].name: img}
outputs = self.sess.run(None, ort_inputs)
return {
"f_border": outputs[0],
"f_char": outputs[1],
"f_direction": outputs[2],
"f_score": outputs[3],
}
def clip_boxes(self, boxes, shape):
h, w = shape[:2]
clipped = []
for box in boxes:
box[:, 0] = np.clip(box[:, 0], 0, w - 1)
box[:, 1] = np.clip(box[:, 1], 0, h - 1)
clipped.append(box)
return np.array(clipped)
def postprocess(self, preds, shape_list):
result = self.pgpostprocess(preds, shape_list)
pts, texts = result["points"], result["texts"]
return self.clip_boxes(pts, self.ori_im.shape), texts
def infer(self, img):
img_input, shape = self.preprocess(img)
preds = self.predict(img_input)
return self.postprocess(preds, shape)
def draw_results(frame, boxes, texts):
for box, text in zip(boxes, texts):
box = box.astype(int).reshape(-1, 1, 2)
cv2.polylines(frame, [box], True, (255,255,0), 2)
cv2.putText(frame, text, tuple(box[0][0]), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0,255,0), 2)
return frame
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PGNet Video OCR")
parser.add_argument("--model", type=str, required=True)
parser.add_argument("--video", type=str, required=True)
parser.add_argument("--cpu", action="store_true")
args = parser.parse_args()
predictor = PGNetPredictor(args.model, args.cpu)
cap = cv2.VideoCapture(args.video)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
out_name = os.path.splitext(os.path.basename(args.video))[0] + "_pgnet_output.mp4"
out_path = os.path.join(os.path.dirname(args.video), out_name)
writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
print(f"▶ Processing video... (Output: {out_path})")
while True:
ret, frame = cap.read()
if not ret:
break
boxes, texts = predictor.infer(frame)
frame = draw_results(frame, boxes, texts)
writer.write(frame)
cap.release()
writer.release()
print("🎉 Done! Video saved:", out_path)

39
YOLO_FACE_EXP/dataset.py
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import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
def get_face_dataloaders(data_dir, batch_size=64, num_workers=4):
"""
Input: data_dir (e.g., ~/face_exp/datasets)
Structure assumed: data_dir/CASIA-WebFace/ID/images.jpg
"""
# 1. Train Transform (Augmentation + Resize to 128x128)
train_transform = transforms.Compose([
transforms.Resize((128, 128), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# 2. Path Setup
train_dir = os.path.join(data_dir, 'CASIA-WebFace')
if not os.path.exists(train_dir):
raise FileNotFoundError(f"데이터셋 경로를 찾을 수 없습니다: {train_dir}\n'CASIA-WebFace' 폴더가 해당 위치에 있는지 확인해주세요.")
# 3. Dataset & Loader
train_dataset = datasets.ImageFolder(root=train_dir, transform=train_transform)
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True
)
return train_loader, len(train_dataset.classes)

71
YOLO_FACE_EXP/export_onnx.py
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import torch
import argparse
import os
from model import get_face_model
def export_to_onnx(pt_path, output_path):
print(f"🔄 Loading model from: {pt_path}")
# 1. 모델 구조 불러오기 및 가중치 로드
# 보드용 모델이므로 학습용 ArcFace 헤더는 버리고, Backbone만 가져옵니다.
model = get_face_model()
# CPU로 로드 (변환은 굳이 GPU 불필요)
checkpoint = torch.load(pt_path, map_location='cpu')
# state_dict 로드 (혹시 모를 키 불일치 방지를 위해 strict=False는 선택사항이나, 여기선 구조가 같으므로 True 권장)
try:
model.load_state_dict(checkpoint)
except RuntimeError as e:
print(f"⚠️ Key mismatch detected. Trying to load with strict=False...")
model.load_state_dict(checkpoint, strict=False)
# 2. Eval 모드 전환 (매우 중요)
# 이걸 안 하면 BatchNorm, Dropout 등이pip 학습 모드로 동작하여 결과가 이상해집니다.
model.eval()
# 3. Dummy Input 생성 (Static Shape: 128x128)
# 보드 사양에 맞춰 배치 사이즈는 1로 고정합니다. [1, 3, 128, 128]
dummy_input = torch.randn(1, 3, 128, 128)
print(f"Target ONNX Path: {output_path}")
# 4. ONNX Export
# external_data=False는 PyTorch export에서 기본적으로 2GB 미만 모델에 대해 적용되어 단일 파일로 나옵니다.
# dynamic_axes 옵션을 뺌으로써 Static Shape을 강제합니다.
torch.onnx.export(
model, # 실행될 모델
dummy_input, # 모델 입력값 (차원 체크용)
output_path, # 저장될 경로
# export_params=True, # 모델 파일 안에 웨이트 저장 (external_data=False 효과)
# opset_version=11, # 임베디드 보드에서 가장 호환성 좋은 버전 (11 추천)
# do_constant_folding=True, # 상수 폴딩 최적화
input_names=['input'], # 입력 노드 이름
output_names=['output'], # 출력 노드 이름
external_data=False
# dynamic_axes={...} <-- 이 옵션을 사용하지 않음으로써 Static Shape으로 고정됨!
)
print(f"✅ Conversion Completed! Model saved at: {output_path}")
print(f"ℹ️ Input Shape: {dummy_input.shape} (Static)")
print(f"ℹ️ Please check if '{output_path}' is a single file.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Convert PyTorch model to ONNX')
# 입력받을 .pt 파일 경로
parser.add_argument('--input', type=str, required=True, help='Input .pt file path')
# 출력할 .onnx 파일 경로 (옵션)
parser.add_argument('--output', type=str, default=None, help='Output .onnx file path')
args = parser.parse_args()
# Output 경로가 없으면 Input 경로에서 확장자만 바꿔서 자동 지정
if args.output is None:
args.output = args.input.replace('.pt', '.onnx')
if not os.path.exists(args.input):
print(f"❌ Error: Input file not found: {args.input}")
else:
export_to_onnx(args.input, args.output)

2767
YOLO_FACE_EXP/face_recog_exp.ipynb
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149
YOLO_FACE_EXP/model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# --------------------------------------------------------
# Basic Blocks (Quantization Friendly: ReLU used)
# --------------------------------------------------------
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
# Kernel size restricted to 1 and 3 for board compatibility
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
# self.relu = nn.ReLU(inplace=True) # PReLU -> ReLU
self.relu = nn.ReLU(inplace=False) # PReLU -> ReLU
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# --------------------------------------------------------
# Backbone: Modified ResNet-50
# --------------------------------------------------------
class ResNetFace(nn.Module):
def __init__(self, block, layers, embedding_size=128):
super(ResNetFace, self).__init__()
self.inplanes = 64
# Stem Block: 7x7 replaced by three 3x3 convs
self.stem = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
# nn.ReLU(inplace=True),
nn.ReLU(inplace=False),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
# nn.ReLU(inplace=True),
nn.ReLU(inplace=False),
nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
# nn.ReLU(inplace=True),
nn.ReLU(inplace=False),
)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
# Output Head: 128x128 Input -> 4x4 Feature Map
self.avgpool = nn.AvgPool2d(4) # Result: 1x1
# FC layer replaced by 1x1 Conv for 128-d embedding
self.fc_conv = nn.Conv2d(512 * block.expansion, embedding_size, kernel_size=1, bias=False)
self.bn_last = nn.BatchNorm2d(embedding_size)
# Init weights
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.stem(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.fc_conv(x)
x = self.bn_last(x) # Output: [N, 128, 1, 1]
return x
# --------------------------------------------------------
# ArcFace Loss Header
# --------------------------------------------------------
class ArcFace(nn.Module):
def __init__(self, in_features, out_features, s=64.0, m=0.50):
super(ArcFace, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.s = s
self.m = m
self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
nn.init.xavier_uniform_(self.weight)
def forward(self, input, label):
# Flatten [N, 128, 1, 1] -> [N, 128] for loss calculation
embedding = input.view(input.size(0), -1)
cosine = F.linear(F.normalize(embedding), F.normalize(self.weight))
# Stable implementation of ArcFace
theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7))
target_logits = torch.cos(theta + self.m)
one_hot = torch.zeros_like(cosine)
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
output = one_hot * target_logits + (1.0 - one_hot) * cosine
output *= self.s
return output
def get_face_model():
return ResNetFace(Bottleneck, [3, 4, 6, 3], embedding_size=128)

120
YOLO_FACE_EXP/train_ver1.py
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import torch
import torch.nn as nn
import torch.optim as optim
import os
import argparse
from tqdm import tqdm
from datetime import datetime # 시간 정보를 가져오기 위해 추가
# Import our custom modules
from model import get_face_model, ArcFace
from dataset import get_face_dataloaders
def main():
# --------------------------------------------------------
# 1. Hyperparameters & Settings
# --------------------------------------------------------
parser = argparse.ArgumentParser(description='Face Recognition Training for Apache 6')
# 데이터셋 경로 (사용자 환경에 맞게 기본값 설정)
parser.add_argument('--data_dir', type=str, default='/home/cuuva/face_exp/datasets', help='Path to datasets')
# 결과 저장 최상위 경로 (여기 아래에 시간별 폴더가 생김)
parser.add_argument('--project_dir', type=str, default='./results', help='Base directory for results')
parser.add_argument('--epochs', type=int, default=20, help='Number of epochs')
parser.add_argument('--batch_size', type=int, default=64, help='Batch size')
parser.add_argument('--lr', type=float, default=0.1, help='Learning rate')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}")
# --------------------------------------------------------
# [수정됨] 실험 디렉토리 생성 로직 (yy-mm-dd-hour-minute)
# --------------------------------------------------------
# 현재 시간 구하기
current_time = datetime.now().strftime("%y-%m-%d-%H-%M")
# 최종 저장 경로: ./results/23-12-12-15-30/
save_dir = os.path.join(args.project_dir, current_time)
# 폴더 생성
os.makedirs(save_dir, exist_ok=True)
print(f"✅ Experiment results will be saved to: {save_dir}")
# --------------------------------------------------------
# 2. Load Data
# --------------------------------------------------------
print("Loading Data...")
try:
train_loader, num_classes = get_face_dataloaders(args.data_dir, args.batch_size)
print(f"Classes (People): {num_classes}, Batch Size: {args.batch_size}")
except Exception as e:
print(f"❌ Error loading data: {e}")
return
# --------------------------------------------------------
# 3. Initialize Model & Loss
# --------------------------------------------------------
# Backbone (보드에 배포할 모델)
backbone = get_face_model().to(device)
# ArcFace Header (학습용 Loss 계산기)
metric_fc = ArcFace(in_features=128, out_features=num_classes).to(device)
# Loss
criterion = nn.CrossEntropyLoss()
# Optimizer
optimizer = optim.SGD([
{'params': backbone.parameters()},
{'params': metric_fc.parameters()}
], lr=args.lr, momentum=0.9, weight_decay=5e-4)
# Scheduler
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[8, 14, 18], gamma=0.1)
# --------------------------------------------------------
# 4. Training Loop
# --------------------------------------------------------
print("🚀 Start Training...")
for epoch in range(args.epochs):
backbone.train()
metric_fc.train()
running_loss = 0.0
pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{args.epochs}")
for images, labels in pbar:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
# Forward Pass
features = backbone(images) # [N, 128, 1, 1]
outputs = metric_fc(features, labels) # [N, Num_Classes]
# Loss Calc & Backward
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
pbar.set_postfix({'loss': running_loss / (pbar.n + 1)})
scheduler.step()
# [수정됨] Save Checkpoint (.pt 확장자 사용)
# 해당 실험 폴더(save_dir) 안에 저장됨
save_path = os.path.join(save_dir, f"backbone_epoch_{epoch+1}.pt")
torch.save(backbone.state_dict(), save_path)
# 마지막 에폭일 때 로그 출력
if epoch == args.epochs - 1:
print(f"🎉 Training Finished! Final model saved at: {save_path}")
if __name__ == "__main__":
main()

141
YOLO_FACE_EXP/train_ver2.py
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import torch
import torch.nn as nn
import torch.optim as optim
import os
import argparse
from tqdm import tqdm
from datetime import datetime
from torchvision import transforms
from torch.utils.data import DataLoader
# Import Custom Modules
from model import get_face_model, ArcFace
from dataset import get_face_dataloaders
from validation import LFWDataset, validate_lfw
def main():
# 1. Hyperparameters
parser = argparse.ArgumentParser(description='Face Recognition Training with LFW Validation')
parser.add_argument('--data_dir', type=str, default='/home/cuuva/face_exp/datasets', help='Root dataset dir')
parser.add_argument('--project_dir', type=str, default='./results', help='Save dir')
parser.add_argument('--epochs', type=int, default=20)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--lr', type=float, default=0.1)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}")
# 디렉토리 설정
current_time = datetime.now().strftime("%y-%m-%d-%H-%M")
base_save_dir = os.path.join(args.project_dir, current_time)
best_save_dir = os.path.join(base_save_dir, 'best_model')
os.makedirs(best_save_dir, exist_ok=True)
print(f"✅ Save path: {base_save_dir}")
# 2. Train Data Loader
print("Loading Train Data...")
try:
train_loader, num_classes = get_face_dataloaders(args.data_dir, args.batch_size)
except Exception as e:
print(f"❌ Train Data Error: {e}")
return
# --------------------------------------------------------
# [수정됨] 3. LFW Validation Loader (경로 로직 개선)
# --------------------------------------------------------
print("Loading LFW Data...")
# pairs.txt가 있는 루트 경로: ~/face_exp/datasets/LFW
lfw_root = os.path.join(args.data_dir, 'LFW')
pairs_path = os.path.join(lfw_root, 'pairs.txt')
# 실제 이미지가 있는 경로 찾기
# 1순위: ~/face_exp/datasets/LFW/lfw (보여주신 구조)
# 2순위: ~/face_exp/datasets/LFW (일반적인 구조)
if os.path.exists(os.path.join(lfw_root, 'lfw')):
lfw_img_dir = os.path.join(lfw_root, 'lfw')
else:
lfw_img_dir = lfw_root
print(f"ℹ️ Pairs path: {pairs_path}")
print(f"ℹ️ Images path: {lfw_img_dir}")
if os.path.exists(pairs_path) and os.path.exists(lfw_img_dir):
lfw_transform = transforms.Compose([
transforms.Resize((128, 128), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# 여기서 lfw_img_dir를 넘겨주는 것이 핵심!
lfw_dataset = LFWDataset(lfw_img_dir, pairs_path, transform=lfw_transform)
lfw_loader = DataLoader(lfw_dataset, batch_size=64, shuffle=False, num_workers=4, drop_last=False)
do_validation = True
print(f"✅ LFW Loaded. Pairs: {len(lfw_dataset)}")
else:
print(f"⚠️ Warning: 'pairs.txt' or Image dir not found. Skipping Validation.")
do_validation = False
# 4. Model & Loss
backbone = get_face_model().to(device)
metric_fc = ArcFace(in_features=128, out_features=num_classes).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD([
{'params': backbone.parameters()},
{'params': metric_fc.parameters()}
], lr=args.lr, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[8, 14, 18], gamma=0.1)
# 5. Training Loop
print("🚀 Start Training...")
best_acc = 0.0
for epoch in range(args.epochs):
# --- TRAIN ---
backbone.train()
metric_fc.train()
running_loss = 0.0
pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{args.epochs}")
for images, labels in pbar:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
features = backbone(images)
outputs = metric_fc(features, labels)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
pbar.set_postfix({'loss': f"{running_loss / (pbar.n + 1):.4f}"})
scheduler.step()
# --- VALIDATION (LFW) ---
if do_validation:
acc, th = validate_lfw(backbone, lfw_loader, device)
print(f"📊 LFW Acc: {acc*100:.2f}% (Threshold: {th:.2f})")
if acc > best_acc:
best_acc = acc
save_path = os.path.join(best_save_dir, "best_backbone.pt")
torch.save(backbone.state_dict(), save_path)
print(f"🏆 Best Model Updated! Saved to {save_path}")
else:
save_path = os.path.join(base_save_dir, f"backbone_epoch_{epoch+1}.pt")
torch.save(backbone.state_dict(), save_path)
torch.save(backbone.state_dict(), os.path.join(base_save_dir, "last_backbone.pt"))
print("\n🎉 Training Finished!")
if __name__ == "__main__":
main()

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YOLO_FACE_EXP/validation.py
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import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import os
import numpy as np
# --------------------------------------------------------
# 1. LFW Dataset Loader
# --------------------------------------------------------
class LFWDataset(Dataset):
def __init__(self, lfw_dir, pairs_path, transform=None):
self.lfw_dir = lfw_dir
self.pairs_path = pairs_path
self.transform = transform
self.validation_images = self.get_lfw_paths(lfw_dir)
def get_lfw_paths(self, lfw_dir):
# pairs.txt 파싱하여 이미지 경로 쌍과 정답(issame) 리스트 생성
pairs = []
with open(self.pairs_path, 'r') as f:
lines = f.readlines()[1:] # 첫 줄(헤더) 건너뜀
for line in lines:
p = line.strip().split('\t')
if len(p) == 3: # 같은 사람 (name, img1_num, img2_num)
name = p[0]
img1 = os.path.join(lfw_dir, name, f"{name}_{int(p[1]):04d}.jpg")
img2 = os.path.join(lfw_dir, name, f"{name}_{int(p[2]):04d}.jpg")
issame = True
pairs.append((img1, img2, issame))
elif len(p) == 4: # 다른 사람 (name1, img1_num, name2, img2_num)
name1 = p[0]
img1 = os.path.join(lfw_dir, name1, f"{name1}_{int(p[1]):04d}.jpg")
name2 = p[2]
img2 = os.path.join(lfw_dir, name2, f"{name2}_{int(p[3]):04d}.jpg")
issame = False
pairs.append((img1, img2, issame))
return pairs
def __len__(self):
return len(self.validation_images)
def __getitem__(self, index):
img1_path, img2_path, issame = self.validation_images[index]
try:
img1 = Image.open(img1_path).convert('RGB')
img2 = Image.open(img2_path).convert('RGB')
except Exception as e:
# 혹시 파일이 없을 경우를 대비한 더미 (실제론 파일 확인 필요)
print(f"File Load Error: {e}")
img1 = Image.new('RGB', (128, 128))
img2 = Image.new('RGB', (128, 128))
if self.transform:
img1 = self.transform(img1)
img2 = self.transform(img2)
return img1, img2, issame
# --------------------------------------------------------
# 2. Evaluation Function
# --------------------------------------------------------
def validate_lfw(model, lfw_loader, device):
model.eval()
similarities = []
actual_issame = []
print("🔍 Validating on LFW...")
with torch.no_grad():
for img1, img2, issame in lfw_loader:
img1, img2 = img1.to(device), img2.to(device)
# Feature Extraction
feat1 = model(img1) # [B, 128, 1, 1]
feat2 = model(img2) # [B, 128, 1, 1]
# Flatten
feat1 = feat1.view(feat1.size(0), -1)
feat2 = feat2.view(feat2.size(0), -1)
# Cosine Similarity Calculation
# 128차원 벡터의 코사인 유사도 (-1 ~ 1)
cos_sim = F.cosine_similarity(feat1, feat2)
similarities.extend(cos_sim.cpu().numpy())
actual_issame.extend(issame.numpy())
similarities = np.array(similarities)
actual_issame = np.array(actual_issame)
# ----------------------------------------------------
# Best Threshold Search (단순화된 버전)
# ----------------------------------------------------
best_acc = 0.0
best_th = 0.0
# -1.0 부터 1.0 까지 0.01 단위로 Threshold를 이동하며 정확도 측정
thresholds = np.arange(-1.0, 1.0, 0.01)
for th in thresholds:
# th보다 크면 True(동일인), 작으면 False(타인) 예측
predict_issame = np.greater(similarities, th)
# 정답과 비교
true_positives = np.sum(np.logical_and(predict_issame, actual_issame))
true_negatives = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
acc = (true_positives + true_negatives) / len(actual_issame)
if acc > best_acc:
best_acc = acc
best_th = th
return best_acc, best_th

9
coco5class_exp/coco5class.yaml
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# path: /home/cuuva/experiment/datasets/VisDrone # 데이터 경로
train: /home/cuuva/experiment/datasets/coco5class/images/train
val: /home/cuuva/experiment/datasets/coco5class/images/val
test: /home/cuuva/experiment/datasets/coco5class/images/test
# nc: 7
nc: 5
names: ['person','car', 'truck', 'bus', 'motor']

342
coco5class_exp/coco5class_exp.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "6b410dab",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f66929b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e5e2ab7b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"🔍 Checking split: train (files: 117266)\n",
"\n",
"🔍 Checking split: val (files: 4952)\n",
"\n",
"📌 클래스 통계 결과:\n",
" - Class 0: 268029 개\n",
" - Class 1: 45449 개\n",
" - Class 2: 10384 개\n",
" - Class 3: 6344 개\n",
" - Class 4: 9021 개\n",
"\n",
"총 클래스 종류: 5\n"
]
}
],
"source": [
"import os\n",
"from collections import Counter\n",
"\n",
"label_root = \"/home/cuuva/experiment/datasets/coco5class/labels\"\n",
"splits = [\"train\", \"val\"]\n",
"\n",
"class_counter = Counter()\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" \n",
" # 라벨 txt 파일 탐색\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
" \n",
" print(f\"\\n🔍 Checking split: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip(): # 빈 줄 제거\n",
" class_id = line.split()[0] # 첫번째 값 = 클래스\n",
" class_counter[class_id] += 1\n",
"\n",
"# 최종 결과 출력\n",
"print(\"\\n📌 클래스 통계 결과:\")\n",
"for cls, count in sorted(class_counter.items(), key=lambda x: int(x[0])):\n",
" print(f\" - Class {cls}: {count} 개\")\n",
"\n",
"print(f\"\\n총 클래스 종류: {len(class_counter)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bb38c03",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New https://pypi.org/project/ultralytics/8.3.235 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/coco5class_exp/coco5class.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=300, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=5class, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=40, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=coco5class_v8m, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=5\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2] \n",
" 1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2] \n",
" 2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True] \n",
" 3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] \n",
" 4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True] \n",
" 5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2] \n",
" 6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True] \n",
" 7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2] \n",
" 8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True] \n",
" 9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2] \n",
" 16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2] \n",
" 19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] \n",
" 22 [15, 18, 21] 1 3778591 ultralytics.nn.modules.head.Detect [5, [192, 384, 576]] \n",
"Model summary: 169 layers, 25,859,215 parameters, 25,859,199 gradients, 79.1 GFLOPs\n",
"\n",
"Transferred 469/475 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 8101.9±2264.5 MB/s, size: 195.4 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/coco5class/labels/train.cache... 117266 images, 48605 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 118287/118287 192.2Mit/s 0.0s\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 0.24G reserved, 0.23G allocated, 30.86G free\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cuuva/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
" 25859215 79.08 2.261 30.79 146.6 (1, 3, 640, 640) list\n",
" 25859215 158.2 3.569 9.957 33.28 (2, 3, 640, 640) list\n",
" 25859215 316.3 4.731 13.44 46.58 (4, 3, 640, 640) list\n",
" 25859215 632.6 7.785 25.68 70.42 (8, 3, 640, 640) list\n",
" 25859215 1265 10.414 27.14 117.5 (16, 3, 640, 640) list\n",
" 25859215 2531 20.416 55.02 239.2 (32, 3, 640, 640) list\n",
" 25859215 5061 36.937 116.3 435.7 (64, 3, 640, 640) list\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 29 for CUDA:0 19.03G/31.33G (61%) ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 6524.6±3206.6 MB/s, size: 190.9 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/coco5class/labels/train.cache... 117266 images, 48605 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 118287/118287 217.1Mit/s 0.0s\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 4736.1±2206.1 MB/s, size: 185.8 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/coco5class/labels/val.cache... 4952 images, 2049 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5000/5000 6.6Mit/s 0.0s0s\n",
"Plotting labels to /home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.000453125), 83 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1m/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class\u001b[0m\n",
"Starting training for 300 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 1/300 14.1G 1.156 1.187 1.242 133 640: 100% ━━━━━━━━━━━━ 4079/4079 3.3it/s 20:20<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 8.9it/s 9.8s0.1s\n",
" all 5000 13759 0.471 0.374 0.387 0.237\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 2/300 14.1G 1.204 1.229 1.282 98 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:06<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 8.6it/s 10.1s0.1s\n",
" all 5000 13759 0.615 0.494 0.54 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 3/300 14.1G 1.172 1.165 1.258 120 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:08<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 8.9it/s 9.8s<0.2s\n",
" all 5000 13759 0.644 0.526 0.58 0.395\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 4/300 14.1G 1.127 1.088 1.231 86 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:06<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.2it/s 9.4s<0.2s\n",
" all 5000 13759 0.671 0.559 0.616 0.424\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 5/300 14.1G 1.088 1.025 1.204 107 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:01<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.3it/s 9.3s<0.1s\n",
" all 5000 13759 0.675 0.58 0.641 0.453\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 6/300 14.1G 1.057 0.9789 1.185 162 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:01<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.4it/s 9.3s<0.1s\n",
" all 5000 13759 0.71 0.575 0.656 0.467\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 7/300 14.1G 1.036 0.9461 1.172 121 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 20:01<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.4it/s 9.3s<0.1s\n",
" all 5000 13759 0.725 0.588 0.672 0.48\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 8/300 14.1G 1.023 0.9197 1.161 105 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 19:60<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.4it/s 9.2s0.1ss\n",
" all 5000 13759 0.735 0.587 0.681 0.488\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 9/300 14.1G 1.009 0.9032 1.15 136 640: 100% ━━━━━━━━━━━━ 4079/4079 3.4it/s 19:60<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 87/87 9.2it/s 9.5s<0.1s\n",
" all 5000 13759 0.719 0.603 0.683 0.493\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 10/300 14.1G 0.9986 0.8829 1.145 136 640: 82% ━━━━━━━━━╸── 3329/4079 3.4it/s 16:20<3:401"
]
}
],
"source": [
"# Load a pretrained YOLO11n model\n",
"model = YOLO('yolov8m.pt')\n",
"\n",
"train_results = model.train(\n",
" data=\"/home/cuuva/experiment/coco5class_exp/coco5class.yaml\", #['person','car', 'truck', 'bus', 'motor']\n",
" epochs=300,\n",
" imgsz=640,\n",
" batch=-1,\n",
" device=\"cuda\",\n",
" optimizer = 'AdamW',\n",
" lr0 = 0.001,\n",
" patience = 40,\n",
" project = 'coco5class_v8m',\n",
" name = '5class',\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c87a7d82",
"metadata": {},
"source": [
"'''\n",
"\n",
"all : 0.559\n",
"\n",
"person : 0.615\n",
"\n",
"car : 0.496\n",
"\n",
"truck : 0.438\n",
"\n",
"bus : 0.739\n",
"\n",
"motor : 0.509\n",
"\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "681c71ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"Model summary (fused): 92 layers, 25,842,655 parameters, 0 gradients, 78.7 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 9, 8400) (49.6 MB)\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 1.3s, saved as '/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.onnx' (98.8 MB)\n",
"\n",
"Export complete (1.6s)\n",
"Results saved to \u001b[1m/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights\u001b[0m\n",
"Predict: yolo predict task=detect model=/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.onnx imgsz=640 \n",
"Validate: yolo val task=detect model=/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.onnx imgsz=640 data=/home/cuuva/experiment/coco5class_exp/coco5class.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"data": {
"text/plain": [
"'/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.onnx'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = YOLO(\"/home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class/weights/best_coco5class.pt\")\n",
"model.export(format=\"onnx\", imgsz=640, device=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b48641",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

106
coco5class_exp/coco5class_v8m/5class/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/coco5class_exp/coco5class.yaml
epochs: 300
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: coco5class_v8m
name: 5class
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/coco5class_exp/coco5class_v8m/5class

91
coco5class_exp/coco5class_v8m/5class/results.csv
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@ -0,0 +1,91 @@
epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,1229.61,1.15644,1.1869,1.24197,0.47072,0.37359,0.38684,0.23748,1.32843,1.32885,1.37945,0.0670081,0.000333252,0.000333252
2,2446.95,1.20417,1.2292,1.28156,0.61506,0.49446,0.54033,0.35796,1.1217,0.9894,1.21888,0.0340059,0.000664385,0.000664385
3,3665.73,1.17197,1.16455,1.25771,0.64365,0.52586,0.57993,0.39502,1.07308,0.91944,1.18859,0.00100149,0.000993319,0.000993319
4,4882.09,1.12694,1.08769,1.23057,0.67081,0.55916,0.61624,0.42356,1.02817,0.85259,1.16029,0.0009901,0.0009901,0.0009901
5,6093.15,1.08789,1.02532,1.2045,0.67548,0.58031,0.64085,0.45344,0.99252,0.81462,1.13196,0.0009868,0.0009868,0.0009868
6,7303.44,1.05683,0.97886,1.18507,0.70964,0.57547,0.65649,0.46687,0.97583,0.7861,1.122,0.0009835,0.0009835,0.0009835
7,8514.17,1.03632,0.94608,1.17203,0.72479,0.58834,0.67208,0.48018,0.96537,0.77084,1.11509,0.0009802,0.0009802,0.0009802
8,9723.56,1.02252,0.91965,1.16113,0.73526,0.58679,0.68078,0.48827,0.95671,0.76109,1.10845,0.0009769,0.0009769,0.0009769
9,10933.1,1.00922,0.90315,1.15043,0.71924,0.6026,0.68299,0.49255,0.94869,0.75524,1.10165,0.0009736,0.0009736,0.0009736
10,12143,0.99897,0.88175,1.14322,0.7326,0.60185,0.68718,0.49649,0.94452,0.75279,1.09849,0.0009703,0.0009703,0.0009703
11,13352.4,0.98834,0.87084,1.13633,0.74018,0.60825,0.69146,0.5012,0.94039,0.7534,1.09568,0.000967,0.000967,0.000967
12,14562.5,0.98102,0.85617,1.1302,0.75324,0.60639,0.69659,0.5054,0.93718,0.75394,1.09293,0.0009637,0.0009637,0.0009637
13,15772.7,0.97347,0.84465,1.12479,0.74515,0.61443,0.69847,0.50866,0.93383,0.75656,1.09052,0.0009604,0.0009604,0.0009604
14,16982.4,0.96684,0.83633,1.12128,0.76161,0.60171,0.70019,0.5114,0.93049,0.75748,1.08756,0.0009571,0.0009571,0.0009571
15,18192,0.95844,0.82148,1.11621,0.77139,0.60114,0.70214,0.51338,0.92661,0.75567,1.0841,0.0009538,0.0009538,0.0009538
16,19401.6,0.95559,0.81514,1.1125,0.77843,0.60163,0.70578,0.5173,0.92289,0.75131,1.08134,0.0009505,0.0009505,0.0009505
17,20612.2,0.94744,0.80473,1.10934,0.77587,0.60808,0.70924,0.52165,0.91891,0.74384,1.07826,0.0009472,0.0009472,0.0009472
18,21821.9,0.94327,0.79853,1.10471,0.77412,0.61318,0.71212,0.52466,0.91585,0.73733,1.07592,0.0009439,0.0009439,0.0009439
19,23031.6,0.93986,0.79415,1.10244,0.76682,0.61869,0.71622,0.52834,0.91328,0.72983,1.07339,0.0009406,0.0009406,0.0009406
20,24242.2,0.93772,0.78704,1.09781,0.75892,0.62992,0.71923,0.53047,0.91096,0.72349,1.07089,0.0009373,0.0009373,0.0009373
21,25452.1,0.93122,0.78054,1.09898,0.76838,0.62483,0.72099,0.53249,0.90767,0.71747,1.06822,0.000934,0.000934,0.000934
22,26662.5,0.92711,0.77642,1.09543,0.76712,0.62933,0.72526,0.53645,0.90473,0.71017,1.06649,0.0009307,0.0009307,0.0009307
23,27873.1,0.92602,0.77119,1.09051,0.76479,0.63155,0.72686,0.53869,0.90338,0.70452,1.06445,0.0009274,0.0009274,0.0009274
24,29083.5,0.92307,0.76622,1.09198,0.76451,0.63842,0.73169,0.54056,0.90219,0.69905,1.06316,0.0009241,0.0009241,0.0009241
25,30293.8,0.92128,0.76072,1.08702,0.75967,0.64485,0.73278,0.54366,0.90059,0.69391,1.06133,0.0009208,0.0009208,0.0009208
26,31504,0.91672,0.75566,1.08562,0.7597,0.64874,0.73347,0.54437,0.89909,0.68913,1.06016,0.0009175,0.0009175,0.0009175
27,32714,0.91335,0.75118,1.08307,0.7692,0.64801,0.73511,0.54634,0.89851,0.68654,1.05949,0.0009142,0.0009142,0.0009142
28,33924.7,0.91246,0.74878,1.0828,0.77617,0.64674,0.73688,0.54695,0.89814,0.68354,1.05914,0.0009109,0.0009109,0.0009109
29,35134.9,0.90747,0.74389,1.0798,0.78163,0.64668,0.73865,0.54846,0.89658,0.68056,1.05799,0.0009076,0.0009076,0.0009076
30,36345.6,0.90625,0.73901,1.07938,0.78008,0.656,0.74083,0.54798,0.89601,0.6777,1.05808,0.0009043,0.0009043,0.0009043
31,37556.4,0.90127,0.73366,1.07423,0.78542,0.64873,0.7423,0.5498,0.89484,0.67485,1.05721,0.000901,0.000901,0.000901
32,38767.3,0.90215,0.73312,1.07624,0.78021,0.65463,0.7436,0.5504,0.89391,0.6727,1.05659,0.0008977,0.0008977,0.0008977
33,39977.8,0.90083,0.72933,1.0755,0.77123,0.65935,0.74486,0.55078,0.89279,0.6711,1.05578,0.0008944,0.0008944,0.0008944
34,41188.5,0.89733,0.72643,1.07282,0.77507,0.65591,0.74712,0.55223,0.89173,0.66943,1.05465,0.0008911,0.0008911,0.0008911
35,42399.2,0.89319,0.72212,1.07016,0.77746,0.65924,0.74888,0.55336,0.89082,0.66747,1.05417,0.0008878,0.0008878,0.0008878
36,43609.6,0.89601,0.72119,1.06842,0.7779,0.66037,0.74843,0.55418,0.88987,0.66696,1.05229,0.0008845,0.0008845,0.0008845
37,44819.8,0.89161,0.71751,1.06898,0.76647,0.66811,0.74785,0.5541,0.88967,0.66599,1.05225,0.0008812,0.0008812,0.0008812
38,46030.1,0.88834,0.71369,1.06483,0.76199,0.66677,0.74688,0.55336,0.88975,0.66509,1.0519,0.0008779,0.0008779,0.0008779
39,47240,0.88654,0.71002,1.06369,0.759,0.66741,0.7462,0.55443,0.8897,0.66559,1.05069,0.0008746,0.0008746,0.0008746
40,48450.3,0.88769,0.70945,1.06666,0.77025,0.6617,0.74661,0.55391,0.88964,0.6652,1.05098,0.0008713,0.0008713,0.0008713
41,49659.1,0.88283,0.70275,1.06431,0.77423,0.66271,0.74706,0.55394,0.88961,0.66512,1.05155,0.000868,0.000868,0.000868
42,50869.4,0.88446,0.70161,1.06447,0.77198,0.66632,0.74751,0.55451,0.88969,0.66383,1.052,0.0008647,0.0008647,0.0008647
43,52079.7,0.88044,0.69913,1.06198,0.76891,0.67012,0.74848,0.55617,0.88881,0.66277,1.05144,0.0008614,0.0008614,0.0008614
44,53289.6,0.87873,0.69822,1.06145,0.76365,0.67158,0.74854,0.55606,0.88923,0.66143,1.05153,0.0008581,0.0008581,0.0008581
45,54499.6,0.87671,0.69467,1.05878,0.76278,0.67406,0.74827,0.55471,0.88982,0.66134,1.05183,0.0008548,0.0008548,0.0008548
46,55710,0.87585,0.69042,1.05866,0.7647,0.67788,0.75004,0.55588,0.88888,0.65963,1.05107,0.0008515,0.0008515,0.0008515
47,56919.4,0.87419,0.68743,1.05706,0.76531,0.67797,0.75114,0.55712,0.8882,0.65944,1.04997,0.0008482,0.0008482,0.0008482
48,58129.4,0.87516,0.68633,1.05811,0.75943,0.68193,0.75114,0.55735,0.88722,0.65912,1.04962,0.0008449,0.0008449,0.0008449
49,59339,0.87393,0.68777,1.05446,0.76238,0.68228,0.75119,0.558,0.88583,0.65802,1.04863,0.0008416,0.0008416,0.0008416
50,60549.4,0.86846,0.68081,1.05379,0.77182,0.67862,0.75375,0.55924,0.88599,0.6566,1.04869,0.0008383,0.0008383,0.0008383
51,61759.9,0.86929,0.68082,1.05612,0.76525,0.68041,0.75292,0.55828,0.88586,0.65612,1.04938,0.000835,0.000835,0.000835
52,62969.9,0.86741,0.67951,1.05621,0.76344,0.68311,0.75271,0.55792,0.88607,0.6561,1.04994,0.0008317,0.0008317,0.0008317
53,64180.6,0.86651,0.6766,1.05158,0.75707,0.68068,0.75147,0.55707,0.88635,0.65645,1.0501,0.0008284,0.0008284,0.0008284
54,65390.9,0.8654,0.67588,1.05465,0.76616,0.67917,0.75175,0.55738,0.88645,0.65667,1.0507,0.0008251,0.0008251,0.0008251
55,66601.3,0.86238,0.67184,1.05168,0.75776,0.67996,0.75165,0.55664,0.88622,0.65807,1.05134,0.0008218,0.0008218,0.0008218
56,67812.1,0.86422,0.67377,1.05452,0.75597,0.68119,0.75113,0.5572,0.88592,0.65844,1.05141,0.0008185,0.0008185,0.0008185
57,69023.3,0.86095,0.66912,1.0496,0.76116,0.68014,0.75074,0.5581,0.88502,0.65844,1.05031,0.0008152,0.0008152,0.0008152
58,70233.2,0.85865,0.66823,1.05075,0.77086,0.67439,0.75079,0.55848,0.88532,0.65904,1.05048,0.0008119,0.0008119,0.0008119
59,71443.4,0.85689,0.6616,1.04504,0.76605,0.67181,0.75043,0.5578,0.88513,0.65965,1.05012,0.0008086,0.0008086,0.0008086
60,72653.5,0.85795,0.66168,1.04595,0.76947,0.67209,0.75166,0.55741,0.8847,0.66034,1.04949,0.0008053,0.0008053,0.0008053
61,73864.5,0.85672,0.66057,1.04478,0.7742,0.66643,0.75042,0.55645,0.88588,0.66176,1.04967,0.000802,0.000802,0.000802
62,75074.9,0.85593,0.65982,1.04392,0.75821,0.68037,0.7503,0.55621,0.88474,0.66219,1.0488,0.0007987,0.0007987,0.0007987
63,76285.4,0.85406,0.65689,1.04539,0.76942,0.66988,0.74948,0.55584,0.88397,0.66081,1.04806,0.0007954,0.0007954,0.0007954
64,77495.9,0.85283,0.65283,1.04132,0.76562,0.67586,0.74927,0.55538,0.88428,0.6595,1.04828,0.0007921,0.0007921,0.0007921
65,78706.3,0.85331,0.65549,1.04268,0.77189,0.66881,0.749,0.5559,0.88504,0.65795,1.04898,0.0007888,0.0007888,0.0007888
66,79916.8,0.85181,0.6535,1.04268,0.78362,0.65943,0.748,0.55658,0.88508,0.65798,1.0487,0.0007855,0.0007855,0.0007855
67,81126.9,0.85264,0.65308,1.04284,0.78844,0.65424,0.74864,0.55594,0.88607,0.6574,1.05003,0.0007822,0.0007822,0.0007822
68,82337.8,0.84906,0.64895,1.03985,0.78274,0.65483,0.74873,0.55573,0.88611,0.65579,1.04997,0.0007789,0.0007789,0.0007789
69,83547.9,0.84922,0.64789,1.03878,0.78557,0.65632,0.74839,0.55543,0.88631,0.65478,1.04974,0.0007756,0.0007756,0.0007756
70,84758,0.84793,0.64511,1.03873,0.79071,0.65474,0.74944,0.5559,0.8871,0.65449,1.05048,0.0007723,0.0007723,0.0007723
71,85968.3,0.84672,0.64492,1.04019,0.78804,0.65652,0.75034,0.55648,0.8873,0.65541,1.05106,0.000769,0.000769,0.000769
72,87179.2,0.84476,0.64351,1.03792,0.77847,0.66854,0.75029,0.55665,0.88789,0.65627,1.05094,0.0007657,0.0007657,0.0007657
73,88389.7,0.84464,0.64237,1.03724,0.77847,0.66462,0.75089,0.55685,0.88795,0.65656,1.05085,0.0007624,0.0007624,0.0007624
74,89599.8,0.84379,0.63877,1.035,0.78097,0.6665,0.75104,0.55678,0.88865,0.65718,1.05112,0.0007591,0.0007591,0.0007591
75,90810.1,0.84251,0.63592,1.03402,0.77879,0.67068,0.75178,0.55723,0.88943,0.65686,1.05237,0.0007558,0.0007558,0.0007558
76,92020.3,0.8406,0.63693,1.0333,0.78477,0.66817,0.75119,0.55778,0.88911,0.65728,1.05182,0.0007525,0.0007525,0.0007525
77,93230.3,0.84016,0.63763,1.03131,0.77849,0.67166,0.74961,0.55693,0.88874,0.65756,1.05089,0.0007492,0.0007492,0.0007492
78,94440.8,0.83834,0.63282,1.03352,0.77658,0.66702,0.74947,0.55609,0.88939,0.65681,1.05193,0.0007459,0.0007459,0.0007459
79,95650.4,0.83965,0.63259,1.03296,0.76483,0.67446,0.74878,0.55559,0.88897,0.65739,1.0515,0.0007426,0.0007426,0.0007426
80,96861.1,0.83776,0.63197,1.03153,0.76791,0.67557,0.74974,0.55744,0.88942,0.65712,1.05189,0.0007393,0.0007393,0.0007393
81,98071,0.83808,0.62934,1.03127,0.77543,0.66909,0.74876,0.55675,0.89092,0.65803,1.05278,0.000736,0.000736,0.000736
82,99281.2,0.8356,0.62767,1.02933,0.76295,0.67705,0.74881,0.55611,0.89067,0.65785,1.05234,0.0007327,0.0007327,0.0007327
83,100491,0.83636,0.62705,1.02787,0.76387,0.67536,0.74919,0.55588,0.89049,0.65771,1.0512,0.0007294,0.0007294,0.0007294
84,101702,0.83436,0.62315,1.02817,0.77337,0.6695,0.74976,0.55728,0.89045,0.65776,1.05145,0.0007261,0.0007261,0.0007261
85,102911,0.83495,0.62625,1.02993,0.76445,0.67239,0.74971,0.55698,0.88994,0.6582,1.05206,0.0007228,0.0007228,0.0007228
86,104121,0.83187,0.62416,1.02909,0.76142,0.67786,0.75036,0.55586,0.8914,0.659,1.05314,0.0007195,0.0007195,0.0007195
87,105330,0.83035,0.61933,1.02612,0.75112,0.68516,0.74852,0.55434,0.89238,0.65963,1.05316,0.0007162,0.0007162,0.0007162
88,106540,0.83004,0.61958,1.02573,0.74852,0.68552,0.74878,0.55448,0.89167,0.66021,1.05276,0.0007129,0.0007129,0.0007129
89,107750,0.82947,0.6179,1.02461,0.76123,0.67949,0.74907,0.55446,0.892,0.65969,1.05324,0.0007096,0.0007096,0.0007096
90,108959,0.83,0.61727,1.02388,0.75264,0.67981,0.74969,0.55546,0.89226,0.65823,1.05408,0.0007063,0.0007063,0.0007063
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 1229.61 1.15644 1.1869 1.24197 0.47072 0.37359 0.38684 0.23748 1.32843 1.32885 1.37945 0.0670081 0.000333252 0.000333252
3 2 2446.95 1.20417 1.2292 1.28156 0.61506 0.49446 0.54033 0.35796 1.1217 0.9894 1.21888 0.0340059 0.000664385 0.000664385
4 3 3665.73 1.17197 1.16455 1.25771 0.64365 0.52586 0.57993 0.39502 1.07308 0.91944 1.18859 0.00100149 0.000993319 0.000993319
5 4 4882.09 1.12694 1.08769 1.23057 0.67081 0.55916 0.61624 0.42356 1.02817 0.85259 1.16029 0.0009901 0.0009901 0.0009901
6 5 6093.15 1.08789 1.02532 1.2045 0.67548 0.58031 0.64085 0.45344 0.99252 0.81462 1.13196 0.0009868 0.0009868 0.0009868
7 6 7303.44 1.05683 0.97886 1.18507 0.70964 0.57547 0.65649 0.46687 0.97583 0.7861 1.122 0.0009835 0.0009835 0.0009835
8 7 8514.17 1.03632 0.94608 1.17203 0.72479 0.58834 0.67208 0.48018 0.96537 0.77084 1.11509 0.0009802 0.0009802 0.0009802
9 8 9723.56 1.02252 0.91965 1.16113 0.73526 0.58679 0.68078 0.48827 0.95671 0.76109 1.10845 0.0009769 0.0009769 0.0009769
10 9 10933.1 1.00922 0.90315 1.15043 0.71924 0.6026 0.68299 0.49255 0.94869 0.75524 1.10165 0.0009736 0.0009736 0.0009736
11 10 12143 0.99897 0.88175 1.14322 0.7326 0.60185 0.68718 0.49649 0.94452 0.75279 1.09849 0.0009703 0.0009703 0.0009703
12 11 13352.4 0.98834 0.87084 1.13633 0.74018 0.60825 0.69146 0.5012 0.94039 0.7534 1.09568 0.000967 0.000967 0.000967
13 12 14562.5 0.98102 0.85617 1.1302 0.75324 0.60639 0.69659 0.5054 0.93718 0.75394 1.09293 0.0009637 0.0009637 0.0009637
14 13 15772.7 0.97347 0.84465 1.12479 0.74515 0.61443 0.69847 0.50866 0.93383 0.75656 1.09052 0.0009604 0.0009604 0.0009604
15 14 16982.4 0.96684 0.83633 1.12128 0.76161 0.60171 0.70019 0.5114 0.93049 0.75748 1.08756 0.0009571 0.0009571 0.0009571
16 15 18192 0.95844 0.82148 1.11621 0.77139 0.60114 0.70214 0.51338 0.92661 0.75567 1.0841 0.0009538 0.0009538 0.0009538
17 16 19401.6 0.95559 0.81514 1.1125 0.77843 0.60163 0.70578 0.5173 0.92289 0.75131 1.08134 0.0009505 0.0009505 0.0009505
18 17 20612.2 0.94744 0.80473 1.10934 0.77587 0.60808 0.70924 0.52165 0.91891 0.74384 1.07826 0.0009472 0.0009472 0.0009472
19 18 21821.9 0.94327 0.79853 1.10471 0.77412 0.61318 0.71212 0.52466 0.91585 0.73733 1.07592 0.0009439 0.0009439 0.0009439
20 19 23031.6 0.93986 0.79415 1.10244 0.76682 0.61869 0.71622 0.52834 0.91328 0.72983 1.07339 0.0009406 0.0009406 0.0009406
21 20 24242.2 0.93772 0.78704 1.09781 0.75892 0.62992 0.71923 0.53047 0.91096 0.72349 1.07089 0.0009373 0.0009373 0.0009373
22 21 25452.1 0.93122 0.78054 1.09898 0.76838 0.62483 0.72099 0.53249 0.90767 0.71747 1.06822 0.000934 0.000934 0.000934
23 22 26662.5 0.92711 0.77642 1.09543 0.76712 0.62933 0.72526 0.53645 0.90473 0.71017 1.06649 0.0009307 0.0009307 0.0009307
24 23 27873.1 0.92602 0.77119 1.09051 0.76479 0.63155 0.72686 0.53869 0.90338 0.70452 1.06445 0.0009274 0.0009274 0.0009274
25 24 29083.5 0.92307 0.76622 1.09198 0.76451 0.63842 0.73169 0.54056 0.90219 0.69905 1.06316 0.0009241 0.0009241 0.0009241
26 25 30293.8 0.92128 0.76072 1.08702 0.75967 0.64485 0.73278 0.54366 0.90059 0.69391 1.06133 0.0009208 0.0009208 0.0009208
27 26 31504 0.91672 0.75566 1.08562 0.7597 0.64874 0.73347 0.54437 0.89909 0.68913 1.06016 0.0009175 0.0009175 0.0009175
28 27 32714 0.91335 0.75118 1.08307 0.7692 0.64801 0.73511 0.54634 0.89851 0.68654 1.05949 0.0009142 0.0009142 0.0009142
29 28 33924.7 0.91246 0.74878 1.0828 0.77617 0.64674 0.73688 0.54695 0.89814 0.68354 1.05914 0.0009109 0.0009109 0.0009109
30 29 35134.9 0.90747 0.74389 1.0798 0.78163 0.64668 0.73865 0.54846 0.89658 0.68056 1.05799 0.0009076 0.0009076 0.0009076
31 30 36345.6 0.90625 0.73901 1.07938 0.78008 0.656 0.74083 0.54798 0.89601 0.6777 1.05808 0.0009043 0.0009043 0.0009043
32 31 37556.4 0.90127 0.73366 1.07423 0.78542 0.64873 0.7423 0.5498 0.89484 0.67485 1.05721 0.000901 0.000901 0.000901
33 32 38767.3 0.90215 0.73312 1.07624 0.78021 0.65463 0.7436 0.5504 0.89391 0.6727 1.05659 0.0008977 0.0008977 0.0008977
34 33 39977.8 0.90083 0.72933 1.0755 0.77123 0.65935 0.74486 0.55078 0.89279 0.6711 1.05578 0.0008944 0.0008944 0.0008944
35 34 41188.5 0.89733 0.72643 1.07282 0.77507 0.65591 0.74712 0.55223 0.89173 0.66943 1.05465 0.0008911 0.0008911 0.0008911
36 35 42399.2 0.89319 0.72212 1.07016 0.77746 0.65924 0.74888 0.55336 0.89082 0.66747 1.05417 0.0008878 0.0008878 0.0008878
37 36 43609.6 0.89601 0.72119 1.06842 0.7779 0.66037 0.74843 0.55418 0.88987 0.66696 1.05229 0.0008845 0.0008845 0.0008845
38 37 44819.8 0.89161 0.71751 1.06898 0.76647 0.66811 0.74785 0.5541 0.88967 0.66599 1.05225 0.0008812 0.0008812 0.0008812
39 38 46030.1 0.88834 0.71369 1.06483 0.76199 0.66677 0.74688 0.55336 0.88975 0.66509 1.0519 0.0008779 0.0008779 0.0008779
40 39 47240 0.88654 0.71002 1.06369 0.759 0.66741 0.7462 0.55443 0.8897 0.66559 1.05069 0.0008746 0.0008746 0.0008746
41 40 48450.3 0.88769 0.70945 1.06666 0.77025 0.6617 0.74661 0.55391 0.88964 0.6652 1.05098 0.0008713 0.0008713 0.0008713
42 41 49659.1 0.88283 0.70275 1.06431 0.77423 0.66271 0.74706 0.55394 0.88961 0.66512 1.05155 0.000868 0.000868 0.000868
43 42 50869.4 0.88446 0.70161 1.06447 0.77198 0.66632 0.74751 0.55451 0.88969 0.66383 1.052 0.0008647 0.0008647 0.0008647
44 43 52079.7 0.88044 0.69913 1.06198 0.76891 0.67012 0.74848 0.55617 0.88881 0.66277 1.05144 0.0008614 0.0008614 0.0008614
45 44 53289.6 0.87873 0.69822 1.06145 0.76365 0.67158 0.74854 0.55606 0.88923 0.66143 1.05153 0.0008581 0.0008581 0.0008581
46 45 54499.6 0.87671 0.69467 1.05878 0.76278 0.67406 0.74827 0.55471 0.88982 0.66134 1.05183 0.0008548 0.0008548 0.0008548
47 46 55710 0.87585 0.69042 1.05866 0.7647 0.67788 0.75004 0.55588 0.88888 0.65963 1.05107 0.0008515 0.0008515 0.0008515
48 47 56919.4 0.87419 0.68743 1.05706 0.76531 0.67797 0.75114 0.55712 0.8882 0.65944 1.04997 0.0008482 0.0008482 0.0008482
49 48 58129.4 0.87516 0.68633 1.05811 0.75943 0.68193 0.75114 0.55735 0.88722 0.65912 1.04962 0.0008449 0.0008449 0.0008449
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58 57 69023.3 0.86095 0.66912 1.0496 0.76116 0.68014 0.75074 0.5581 0.88502 0.65844 1.05031 0.0008152 0.0008152 0.0008152
59 58 70233.2 0.85865 0.66823 1.05075 0.77086 0.67439 0.75079 0.55848 0.88532 0.65904 1.05048 0.0008119 0.0008119 0.0008119
60 59 71443.4 0.85689 0.6616 1.04504 0.76605 0.67181 0.75043 0.5578 0.88513 0.65965 1.05012 0.0008086 0.0008086 0.0008086
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62 61 73864.5 0.85672 0.66057 1.04478 0.7742 0.66643 0.75042 0.55645 0.88588 0.66176 1.04967 0.000802 0.000802 0.000802
63 62 75074.9 0.85593 0.65982 1.04392 0.75821 0.68037 0.7503 0.55621 0.88474 0.66219 1.0488 0.0007987 0.0007987 0.0007987
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82 81 98071 0.83808 0.62934 1.03127 0.77543 0.66909 0.74876 0.55675 0.89092 0.65803 1.05278 0.000736 0.000736 0.000736
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16
coco5class_exp/train_coco5class.py
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from ultralytics import YOLO
model = YOLO('yolov8m.pt')
model.train(
data="/home/cuuva/experiment/coco5class_exp/coco5class.yaml",
epochs=300,
imgsz=640,
batch=-1,
device="cuda",
optimizer='AdamW',
lr0=0.001,
patience=40,
project='coco5class_v8m',
name='5class'
)

33
custom_LP_detect/custom_LP.yaml
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# # path: datasets/VisDrone # 데이터 경로
# train: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/image/license_plate/
# val: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/image/license_plate/
# # test: images/test
# # path: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/
# # train:
# # - image/license_plate/ # 이미지 폴더
# # val:
# # - ../Validation/image/license_plate/
# # 각 이미지에 대응하는 라벨 경로를 직접 지정
# labels:
# train: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/labels/license_plate/
# val: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/labels/license_plate/
# nc: 1
# names: ['lp']
# custom_LP.yaml
# 데이터셋 경로
train: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/
val: /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/
# 클래스 수
nc: 1
# 클래스 이름
names: ['lp']

38
custom_LP_detect/easyocr.py
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import easyocr
import cv2
from matplotlib import pyplot as plt
import time # ⬅ 추가
# 1. EasyOCR Reader 생성
reader = easyocr.Reader(['ko', 'en'], gpu=False)
# 2. 이미지 불러오기
# image_path = '/home/cuuva/다운로드/test/ocr_resized.png'
image_path = '/home/cuuva/experiment/custom_LP_detect/ocr2.png'
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# ------------------------
# 3. OCR 수행 및 시간 측정
# ------------------------
start_time = time.time()
results = reader.readtext(image_rgb, detail=1)
end_time = time.time()
print(f"Inference time: {end_time - start_time:.3f} seconds")
# 4. 결과 출력 및 시각화
for (bbox, text, prob) in results:
print(f"Detected text: {text}, Confidence: {prob:.2f}")
# 바운딩 박스
top_left = tuple(map(int, bbox[0]))
bottom_right = tuple(map(int, bbox[2]))
cv2.rectangle(image_rgb, top_left, bottom_right, (0, 255, 0), 2)
cv2.putText(image_rgb, text, (top_left[0], top_left[1]-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
# 5. 시각화
plt.figure(figsize=(10,6))
plt.imshow(image_rgb)
plt.axis('off')
plt.show()

106
custom_LP_detect/lp_detect/epo_100_frac_0_1/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/custom_LP_detect/custom_LP.yaml
epochs: 100
time: null
patience: 30
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: lp_detect
name: epo_100_frac_0_1
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.1
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_100_frac_0_1

49
custom_LP_detect/lp_detect/epo_100_frac_0_1/results.csv
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@ -0,0 +1,49 @@
epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,425.165,2.19122,3.26902,0.82512,0.6796,0.40305,0.46173,0.21578,1.89595,1.23584,0.8364,0.0670556,0.000332772,0.000332772
2,808.781,1.64521,0.75428,0.75617,0.65304,0.41129,0.47132,0.22205,1.90004,1.21189,0.83069,0.034049,0.000659511,0.000659511
3,1231.51,1.52713,0.69414,0.75118,0.66802,0.45492,0.5031,0.24661,1.7403,1.08313,0.81498,0.00103577,0.00097965,0.00097965
4,1691.33,1.43927,0.65043,0.74699,0.65894,0.45254,0.51006,0.25925,1.74713,1.13768,0.81,0.0009703,0.0009703,0.0009703
5,2257.87,1.36285,0.61528,0.74381,0.64243,0.41284,0.47289,0.20355,1.98662,1.21622,0.82419,0.0009604,0.0009604,0.0009604
6,2784.98,1.32167,0.59366,0.7425,0.62479,0.45232,0.49886,0.25337,1.72637,1.15112,0.81075,0.0009505,0.0009505,0.0009505
7,3392.08,1.29427,0.57982,0.74102,0.61384,0.47403,0.50783,0.26403,1.6687,1.12246,0.80667,0.0009406,0.0009406,0.0009406
8,4001.3,1.26971,0.57071,0.74022,0.64064,0.45808,0.51302,0.2696,1.63154,1.13117,0.80421,0.0009307,0.0009307,0.0009307
9,4654.23,1.24975,0.56167,0.74002,0.64587,0.47878,0.53826,0.28473,1.6386,1.03233,0.80128,0.0009208,0.0009208,0.0009208
10,5294.75,1.22996,0.55374,0.73855,0.63738,0.48036,0.52939,0.27517,1.65346,1.05963,0.80323,0.0009109,0.0009109,0.0009109
11,5890.25,1.21637,0.5461,0.73821,0.62227,0.48528,0.53838,0.2876,1.5961,1.05144,0.79948,0.000901,0.000901,0.000901
12,6506.41,1.20813,0.54059,0.73824,0.62561,0.48721,0.53111,0.28706,1.57482,1.05719,0.79905,0.0008911,0.0008911,0.0008911
13,7072.89,1.19407,0.53627,0.73775,0.65056,0.48666,0.54766,0.28064,1.65217,1.00232,0.80387,0.0008812,0.0008812,0.0008812
14,7702.37,1.18192,0.52905,0.73707,0.63316,0.48291,0.53362,0.28604,1.58725,1.05205,0.79932,0.0008713,0.0008713,0.0008713
15,8322.77,1.17533,0.52539,0.7365,0.63463,0.48576,0.53978,0.28057,1.62984,1.04773,0.80217,0.0008614,0.0008614,0.0008614
16,8945.4,1.16706,0.52089,0.73663,0.63386,0.47596,0.53067,0.27845,1.6132,1.05199,0.80074,0.0008515,0.0008515,0.0008515
17,9549.4,1.16206,0.51874,0.7363,0.63145,0.48427,0.54143,0.29506,1.57316,1.02901,0.79965,0.0008416,0.0008416,0.0008416
18,10183,1.15319,0.51494,0.73602,0.63691,0.48989,0.54739,0.29699,1.54797,0.98616,0.79701,0.0008317,0.0008317,0.0008317
19,10864.6,1.14836,0.51207,0.73527,0.63076,0.48931,0.54466,0.28707,1.59721,1.01195,0.79987,0.0008218,0.0008218,0.0008218
20,11543.9,1.13952,0.50831,0.73561,0.63679,0.48773,0.54475,0.2852,1.60959,1.00444,0.8005,0.0008119,0.0008119,0.0008119
21,12149.9,1.13458,0.50721,0.73546,0.6347,0.49139,0.54533,0.28571,1.59979,1.00025,0.80068,0.000802,0.000802,0.000802
22,12807.7,1.13033,0.50404,0.73513,0.64262,0.48644,0.54558,0.28325,1.61616,1.00355,0.80181,0.0007921,0.0007921,0.0007921
23,13457.6,1.12219,0.50083,0.73509,0.63476,0.49074,0.54641,0.28606,1.60342,1.00548,0.80097,0.0007822,0.0007822,0.0007822
24,14045.2,1.12299,0.50079,0.73451,0.63356,0.48964,0.54538,0.2886,1.58839,1.00563,0.80022,0.0007723,0.0007723,0.0007723
25,14682.9,1.11743,0.49773,0.7344,0.63638,0.48854,0.54547,0.28935,1.58982,1.00766,0.80023,0.0007624,0.0007624,0.0007624
26,15313,1.11159,0.49428,0.73421,0.63273,0.49187,0.54717,0.29134,1.5834,1.00419,0.79976,0.0007525,0.0007525,0.0007525
27,15949.4,1.1079,0.49312,0.73477,0.62927,0.49237,0.54658,0.29279,1.57963,1.00732,0.79923,0.0007426,0.0007426,0.0007426
28,16590.8,1.10157,0.49046,0.73422,0.63069,0.49079,0.54587,0.29366,1.57586,1.01084,0.79888,0.0007327,0.0007327,0.0007327
29,17238.4,1.10082,0.48931,0.73398,0.62821,0.49129,0.54513,0.29312,1.58014,1.01325,0.79913,0.0007228,0.0007228,0.0007228
30,17863.7,1.09788,0.4879,0.73401,0.62898,0.49109,0.54562,0.29262,1.58446,1.01239,0.79944,0.0007129,0.0007129,0.0007129
31,18540.5,1.09284,0.48642,0.73404,0.62873,0.49098,0.54562,0.29185,1.58875,1.01304,0.79989,0.000703,0.000703,0.000703
32,19186.3,1.09083,0.48494,0.73416,0.62829,0.49115,0.54536,0.29198,1.5888,1.0143,0.80005,0.0006931,0.0006931,0.0006931
33,19839.8,1.08699,0.48398,0.73355,0.62734,0.49145,0.54532,0.29191,1.58867,1.0148,0.80002,0.0006832,0.0006832,0.0006832
34,20504,1.08597,0.48141,0.73393,0.62698,0.4914,0.54523,0.29118,1.59253,1.01673,0.80031,0.0006733,0.0006733,0.0006733
35,21174.5,1.08151,0.48094,0.7331,0.62684,0.49147,0.54545,0.29099,1.59534,1.01703,0.80055,0.0006634,0.0006634,0.0006634
36,21848.8,1.08102,0.47931,0.73376,0.62743,0.49117,0.54571,0.291,1.59622,1.01618,0.80068,0.0006535,0.0006535,0.0006535
37,22473.2,1.07841,0.47814,0.73345,0.62733,0.49067,0.54546,0.29072,1.59795,1.0186,0.80085,0.0006436,0.0006436,0.0006436
38,23119.6,1.07543,0.47798,0.73324,0.62685,0.49,0.54491,0.29029,1.59986,1.0207,0.80101,0.0006337,0.0006337,0.0006337
39,23782.1,1.073,0.47657,0.7333,0.6263,0.49022,0.54493,0.29014,1.6003,1.02031,0.80113,0.0006238,0.0006238,0.0006238
40,24405.1,1.07203,0.47559,0.73325,0.62578,0.49105,0.54528,0.2901,1.60029,1.01927,0.80117,0.0006139,0.0006139,0.0006139
41,25029.6,1.06935,0.47511,0.73316,0.62636,0.49111,0.54574,0.29017,1.60051,1.01941,0.80117,0.000604,0.000604,0.000604
42,25669.9,1.06678,0.47396,0.7325,0.62768,0.49078,0.54632,0.29068,1.59918,1.01861,0.80112,0.0005941,0.0005941,0.0005941
43,26327.5,1.06205,0.46927,0.73269,0.628,0.49054,0.5466,0.29107,1.59911,1.02009,0.80121,0.0005842,0.0005842,0.0005842
44,27008.8,1.05982,0.47072,0.7323,0.6289,0.49022,0.54753,0.29179,1.59858,1.01947,0.80132,0.0005743,0.0005743,0.0005743
45,27654.8,1.05797,0.46855,0.73287,0.62908,0.48979,0.5479,0.29226,1.59741,1.01905,0.80134,0.0005644,0.0005644,0.0005644
46,28273.5,1.05607,0.46781,0.73253,0.63035,0.4887,0.54799,0.29264,1.5944,1.01842,0.80121,0.0005545,0.0005545,0.0005545
47,28888.8,1.05285,0.46653,0.7322,0.6308,0.48859,0.54824,0.293,1.59415,1.01825,0.8012,0.0005446,0.0005446,0.0005446
48,29666.5,1.05326,0.46586,0.73276,0.63148,0.48755,0.54793,0.29316,1.59252,1.01869,0.80116,0.0005347,0.0005347,0.0005347
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 425.165 2.19122 3.26902 0.82512 0.6796 0.40305 0.46173 0.21578 1.89595 1.23584 0.8364 0.0670556 0.000332772 0.000332772
3 2 808.781 1.64521 0.75428 0.75617 0.65304 0.41129 0.47132 0.22205 1.90004 1.21189 0.83069 0.034049 0.000659511 0.000659511
4 3 1231.51 1.52713 0.69414 0.75118 0.66802 0.45492 0.5031 0.24661 1.7403 1.08313 0.81498 0.00103577 0.00097965 0.00097965
5 4 1691.33 1.43927 0.65043 0.74699 0.65894 0.45254 0.51006 0.25925 1.74713 1.13768 0.81 0.0009703 0.0009703 0.0009703
6 5 2257.87 1.36285 0.61528 0.74381 0.64243 0.41284 0.47289 0.20355 1.98662 1.21622 0.82419 0.0009604 0.0009604 0.0009604
7 6 2784.98 1.32167 0.59366 0.7425 0.62479 0.45232 0.49886 0.25337 1.72637 1.15112 0.81075 0.0009505 0.0009505 0.0009505
8 7 3392.08 1.29427 0.57982 0.74102 0.61384 0.47403 0.50783 0.26403 1.6687 1.12246 0.80667 0.0009406 0.0009406 0.0009406
9 8 4001.3 1.26971 0.57071 0.74022 0.64064 0.45808 0.51302 0.2696 1.63154 1.13117 0.80421 0.0009307 0.0009307 0.0009307
10 9 4654.23 1.24975 0.56167 0.74002 0.64587 0.47878 0.53826 0.28473 1.6386 1.03233 0.80128 0.0009208 0.0009208 0.0009208
11 10 5294.75 1.22996 0.55374 0.73855 0.63738 0.48036 0.52939 0.27517 1.65346 1.05963 0.80323 0.0009109 0.0009109 0.0009109
12 11 5890.25 1.21637 0.5461 0.73821 0.62227 0.48528 0.53838 0.2876 1.5961 1.05144 0.79948 0.000901 0.000901 0.000901
13 12 6506.41 1.20813 0.54059 0.73824 0.62561 0.48721 0.53111 0.28706 1.57482 1.05719 0.79905 0.0008911 0.0008911 0.0008911
14 13 7072.89 1.19407 0.53627 0.73775 0.65056 0.48666 0.54766 0.28064 1.65217 1.00232 0.80387 0.0008812 0.0008812 0.0008812
15 14 7702.37 1.18192 0.52905 0.73707 0.63316 0.48291 0.53362 0.28604 1.58725 1.05205 0.79932 0.0008713 0.0008713 0.0008713
16 15 8322.77 1.17533 0.52539 0.7365 0.63463 0.48576 0.53978 0.28057 1.62984 1.04773 0.80217 0.0008614 0.0008614 0.0008614
17 16 8945.4 1.16706 0.52089 0.73663 0.63386 0.47596 0.53067 0.27845 1.6132 1.05199 0.80074 0.0008515 0.0008515 0.0008515
18 17 9549.4 1.16206 0.51874 0.7363 0.63145 0.48427 0.54143 0.29506 1.57316 1.02901 0.79965 0.0008416 0.0008416 0.0008416
19 18 10183 1.15319 0.51494 0.73602 0.63691 0.48989 0.54739 0.29699 1.54797 0.98616 0.79701 0.0008317 0.0008317 0.0008317
20 19 10864.6 1.14836 0.51207 0.73527 0.63076 0.48931 0.54466 0.28707 1.59721 1.01195 0.79987 0.0008218 0.0008218 0.0008218
21 20 11543.9 1.13952 0.50831 0.73561 0.63679 0.48773 0.54475 0.2852 1.60959 1.00444 0.8005 0.0008119 0.0008119 0.0008119
22 21 12149.9 1.13458 0.50721 0.73546 0.6347 0.49139 0.54533 0.28571 1.59979 1.00025 0.80068 0.000802 0.000802 0.000802
23 22 12807.7 1.13033 0.50404 0.73513 0.64262 0.48644 0.54558 0.28325 1.61616 1.00355 0.80181 0.0007921 0.0007921 0.0007921
24 23 13457.6 1.12219 0.50083 0.73509 0.63476 0.49074 0.54641 0.28606 1.60342 1.00548 0.80097 0.0007822 0.0007822 0.0007822
25 24 14045.2 1.12299 0.50079 0.73451 0.63356 0.48964 0.54538 0.2886 1.58839 1.00563 0.80022 0.0007723 0.0007723 0.0007723
26 25 14682.9 1.11743 0.49773 0.7344 0.63638 0.48854 0.54547 0.28935 1.58982 1.00766 0.80023 0.0007624 0.0007624 0.0007624
27 26 15313 1.11159 0.49428 0.73421 0.63273 0.49187 0.54717 0.29134 1.5834 1.00419 0.79976 0.0007525 0.0007525 0.0007525
28 27 15949.4 1.1079 0.49312 0.73477 0.62927 0.49237 0.54658 0.29279 1.57963 1.00732 0.79923 0.0007426 0.0007426 0.0007426
29 28 16590.8 1.10157 0.49046 0.73422 0.63069 0.49079 0.54587 0.29366 1.57586 1.01084 0.79888 0.0007327 0.0007327 0.0007327
30 29 17238.4 1.10082 0.48931 0.73398 0.62821 0.49129 0.54513 0.29312 1.58014 1.01325 0.79913 0.0007228 0.0007228 0.0007228
31 30 17863.7 1.09788 0.4879 0.73401 0.62898 0.49109 0.54562 0.29262 1.58446 1.01239 0.79944 0.0007129 0.0007129 0.0007129
32 31 18540.5 1.09284 0.48642 0.73404 0.62873 0.49098 0.54562 0.29185 1.58875 1.01304 0.79989 0.000703 0.000703 0.000703
33 32 19186.3 1.09083 0.48494 0.73416 0.62829 0.49115 0.54536 0.29198 1.5888 1.0143 0.80005 0.0006931 0.0006931 0.0006931
34 33 19839.8 1.08699 0.48398 0.73355 0.62734 0.49145 0.54532 0.29191 1.58867 1.0148 0.80002 0.0006832 0.0006832 0.0006832
35 34 20504 1.08597 0.48141 0.73393 0.62698 0.4914 0.54523 0.29118 1.59253 1.01673 0.80031 0.0006733 0.0006733 0.0006733
36 35 21174.5 1.08151 0.48094 0.7331 0.62684 0.49147 0.54545 0.29099 1.59534 1.01703 0.80055 0.0006634 0.0006634 0.0006634
37 36 21848.8 1.08102 0.47931 0.73376 0.62743 0.49117 0.54571 0.291 1.59622 1.01618 0.80068 0.0006535 0.0006535 0.0006535
38 37 22473.2 1.07841 0.47814 0.73345 0.62733 0.49067 0.54546 0.29072 1.59795 1.0186 0.80085 0.0006436 0.0006436 0.0006436
39 38 23119.6 1.07543 0.47798 0.73324 0.62685 0.49 0.54491 0.29029 1.59986 1.0207 0.80101 0.0006337 0.0006337 0.0006337
40 39 23782.1 1.073 0.47657 0.7333 0.6263 0.49022 0.54493 0.29014 1.6003 1.02031 0.80113 0.0006238 0.0006238 0.0006238
41 40 24405.1 1.07203 0.47559 0.73325 0.62578 0.49105 0.54528 0.2901 1.60029 1.01927 0.80117 0.0006139 0.0006139 0.0006139
42 41 25029.6 1.06935 0.47511 0.73316 0.62636 0.49111 0.54574 0.29017 1.60051 1.01941 0.80117 0.000604 0.000604 0.000604
43 42 25669.9 1.06678 0.47396 0.7325 0.62768 0.49078 0.54632 0.29068 1.59918 1.01861 0.80112 0.0005941 0.0005941 0.0005941
44 43 26327.5 1.06205 0.46927 0.73269 0.628 0.49054 0.5466 0.29107 1.59911 1.02009 0.80121 0.0005842 0.0005842 0.0005842
45 44 27008.8 1.05982 0.47072 0.7323 0.6289 0.49022 0.54753 0.29179 1.59858 1.01947 0.80132 0.0005743 0.0005743 0.0005743
46 45 27654.8 1.05797 0.46855 0.73287 0.62908 0.48979 0.5479 0.29226 1.59741 1.01905 0.80134 0.0005644 0.0005644 0.0005644
47 46 28273.5 1.05607 0.46781 0.73253 0.63035 0.4887 0.54799 0.29264 1.5944 1.01842 0.80121 0.0005545 0.0005545 0.0005545
48 47 28888.8 1.05285 0.46653 0.7322 0.6308 0.48859 0.54824 0.293 1.59415 1.01825 0.8012 0.0005446 0.0005446 0.0005446
49 48 29666.5 1.05326 0.46586 0.73276 0.63148 0.48755 0.54793 0.29316 1.59252 1.01869 0.80116 0.0005347 0.0005347 0.0005347

106
custom_LP_detect/lp_detect/epo_200_frac_0_2/args.yaml
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task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/custom_LP_detect/custom_LP.yaml
epochs: 200
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: lp_detect
name: epo_200_frac_0_2
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.2
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
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flipud: 0.0
fliplr: 0.5
bgr: 0.0
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copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_2

120
custom_LP_detect/lp_detect/epo_200_frac_0_2/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,723.844,2.07408,2.21817,0.8098,0.71536,0.48978,0.53151,0.25262,1.73505,0.9444,0.81842,0.0670278,0.000333053,0.000333053
2,1435.55,1.58293,0.74137,0.75875,0.79959,0.52345,0.59289,0.31563,1.52597,0.81303,0.79943,0.0340245,0.000663087,0.000663087
3,2176.86,1.46606,0.67807,0.75444,0.7983,0.51441,0.60086,0.31887,1.55418,0.84581,0.80237,0.00101788,0.000989822,0.000989822
4,2966.54,1.38407,0.63657,0.75046,0.80813,0.53779,0.62884,0.33972,1.4425,0.71651,0.79698,0.00098515,0.00098515,0.00098515
5,3858.8,1.31864,0.60203,0.74793,0.82935,0.55475,0.64409,0.35485,1.41393,0.69283,0.79108,0.0009802,0.0009802,0.0009802
6,4726.74,1.27895,0.58078,0.74608,0.85718,0.561,0.67905,0.39662,1.32381,0.66201,0.78561,0.00097525,0.00097525,0.00097525
7,5601.21,1.25517,0.56979,0.74525,0.83164,0.57179,0.6738,0.38316,1.35157,0.64819,0.78647,0.0009703,0.0009703,0.0009703
8,6485.42,1.23291,0.55671,0.74456,0.84298,0.56348,0.66817,0.38618,1.31,0.64487,0.78634,0.00096535,0.00096535,0.00096535
9,7453.91,1.21657,0.54911,0.74341,0.84957,0.57325,0.68451,0.39491,1.31351,0.63027,0.7854,0.0009604,0.0009604,0.0009604
10,8361.51,1.20022,0.54134,0.74261,0.84781,0.57741,0.68773,0.40126,1.29022,0.61634,0.78321,0.00095545,0.00095545,0.00095545
11,9240.95,1.18858,0.53458,0.74274,0.83864,0.57749,0.68667,0.40412,1.27972,0.61789,0.78315,0.0009505,0.0009505,0.0009505
12,10161.6,1.1797,0.52989,0.74227,0.8378,0.57844,0.68827,0.39961,1.30056,0.61911,0.78465,0.00094555,0.00094555,0.00094555
13,11092.6,1.16883,0.52609,0.74147,0.83417,0.57881,0.68672,0.40123,1.29338,0.61691,0.78423,0.0009406,0.0009406,0.0009406
14,12068,1.16096,0.52096,0.74113,0.83328,0.58014,0.68871,0.40368,1.28619,0.61376,0.7837,0.00093565,0.00093565,0.00093565
15,13026.6,1.15413,0.51717,0.74075,0.83333,0.58048,0.68958,0.40327,1.29169,0.61364,0.78402,0.0009307,0.0009307,0.0009307
16,13913.5,1.14571,0.51344,0.74134,0.83365,0.58055,0.69056,0.40359,1.29405,0.61359,0.78406,0.00092575,0.00092575,0.00092575
17,14779.3,1.14068,0.51065,0.74023,0.83525,0.5819,0.69303,0.4057,1.29086,0.61156,0.78377,0.0009208,0.0009208,0.0009208
18,15716.1,1.13238,0.50791,0.74038,0.83657,0.58256,0.69503,0.4071,1.28866,0.60962,0.78357,0.00091585,0.00091585,0.00091585
19,16629.1,1.12878,0.5051,0.74076,0.83729,0.58302,0.69698,0.40788,1.29062,0.60915,0.78367,0.0009109,0.0009109,0.0009109
20,17521.1,1.12488,0.50285,0.7399,0.83782,0.58433,0.69918,0.41006,1.28735,0.60737,0.78341,0.00090595,0.00090595,0.00090595
21,18492.9,1.11971,0.50015,0.74021,0.83857,0.5858,0.70192,0.41283,1.2834,0.60603,0.7831,0.000901,0.000901,0.000901
22,19399.6,1.11692,0.49908,0.73945,0.83934,0.58661,0.70345,0.41335,1.28588,0.6061,0.78323,0.00089605,0.00089605,0.00089605
23,20294.5,1.11215,0.49759,0.73928,0.83927,0.58738,0.70438,0.41399,1.28643,0.60604,0.78324,0.0008911,0.0008911,0.0008911
24,21151.7,1.10788,0.49421,0.73984,0.84037,0.58837,0.7059,0.41548,1.28522,0.60566,0.78319,0.00088615,0.00088615,0.00088615
25,22088.9,1.10476,0.49299,0.73944,0.84095,0.58922,0.70722,0.41772,1.28132,0.60488,0.78295,0.0008812,0.0008812,0.0008812
26,23013.5,1.10147,0.4912,0.73954,0.84264,0.58976,0.7089,0.41965,1.27934,0.60437,0.78281,0.00087625,0.00087625,0.00087625
27,23959.5,1.09802,0.48966,0.73893,0.84349,0.59073,0.71016,0.42063,1.27834,0.60363,0.78281,0.0008713,0.0008713,0.0008713
28,24847.5,1.0993,0.49022,0.7391,0.84557,0.59096,0.71174,0.42268,1.27449,0.60229,0.78259,0.00086635,0.00086635,0.00086635
29,25860.6,1.093,0.48708,0.73963,0.84576,0.59192,0.71262,0.4233,1.27515,0.60178,0.7827,0.0008614,0.0008614,0.0008614
30,26820.9,1.09207,0.48677,0.73836,0.84635,0.59256,0.71374,0.42469,1.27218,0.6003,0.78259,0.00085645,0.00085645,0.00085645
31,27827.5,1.08848,0.4848,0.73905,0.84662,0.59335,0.71493,0.42536,1.27182,0.5997,0.78257,0.0008515,0.0008515,0.0008515
32,28795.5,1.08572,0.48334,0.73856,0.84686,0.59368,0.71559,0.42569,1.27282,0.59917,0.78265,0.00084655,0.00084655,0.00084655
33,29724.5,1.08286,0.48141,0.73857,0.848,0.59357,0.71659,0.427,1.27097,0.59897,0.7826,0.0008416,0.0008416,0.0008416
34,30631.6,1.07959,0.47956,0.73879,0.84889,0.59343,0.71705,0.4274,1.27074,0.59846,0.78264,0.00083665,0.00083665,0.00083665
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36,32533.2,1.0764,0.47765,0.73841,0.85041,0.5939,0.71861,0.4288,1.26698,0.59606,0.78256,0.00082675,0.00082675,0.00082675
37,33532.7,1.07279,0.47748,0.73848,0.85122,0.59396,0.71889,0.4286,1.26801,0.59593,0.78261,0.0008218,0.0008218,0.0008218
38,34501.6,1.07425,0.47674,0.73852,0.85176,0.59404,0.71884,0.42911,1.26599,0.59374,0.78256,0.00081685,0.00081685,0.00081685
39,35373.3,1.06917,0.47443,0.73859,0.85126,0.5943,0.71847,0.42856,1.2662,0.59232,0.78254,0.0008119,0.0008119,0.0008119
40,36284.4,1.06731,0.47499,0.73819,0.85083,0.59518,0.71814,0.42813,1.26535,0.59079,0.78245,0.00080695,0.00080695,0.00080695
41,37255.6,1.06678,0.4734,0.73795,0.85094,0.59498,0.71785,0.42774,1.26787,0.59061,0.78271,0.000802,0.000802,0.000802
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44,40291.3,1.06134,0.47054,0.73818,0.85007,0.59581,0.71792,0.42701,1.26932,0.58986,0.78306,0.00078715,0.00078715,0.00078715
45,41169.5,1.06046,0.47018,0.73767,0.84986,0.59616,0.71769,0.42698,1.26869,0.58898,0.78312,0.0007822,0.0007822,0.0007822
46,42069.8,1.05857,0.46901,0.73773,0.84951,0.59609,0.71731,0.42719,1.2667,0.58819,0.7831,0.00077725,0.00077725,0.00077725
47,43013,1.05927,0.46992,0.73736,0.84897,0.59562,0.7171,0.42771,1.26447,0.58815,0.7831,0.0007723,0.0007723,0.0007723
48,43920.9,1.05574,0.46823,0.73798,0.84852,0.59561,0.71718,0.42801,1.26351,0.58791,0.78306,0.00076735,0.00076735,0.00076735
49,44775.4,1.05387,0.46688,0.738,0.84847,0.59554,0.71709,0.42861,1.26226,0.58731,0.78301,0.0007624,0.0007624,0.0007624
50,45689.3,1.05387,0.4664,0.73749,0.8488,0.59536,0.71721,0.42878,1.26178,0.58744,0.78312,0.00075745,0.00075745,0.00075745
51,46596.5,1.05139,0.46497,0.73758,0.84891,0.59489,0.71677,0.42893,1.26034,0.58708,0.7831,0.0007525,0.0007525,0.0007525
52,47508.9,1.05,0.46428,0.73772,0.84781,0.59533,0.71665,0.42971,1.25555,0.58558,0.78288,0.00074755,0.00074755,0.00074755
53,48398.7,1.04705,0.46364,0.73732,0.84742,0.5955,0.71633,0.43027,1.25425,0.58563,0.78287,0.0007426,0.0007426,0.0007426
54,49319,1.04838,0.46313,0.73768,0.84778,0.59557,0.71638,0.4307,1.25382,0.58552,0.78285,0.00073765,0.00073765,0.00073765
55,50247,1.04533,0.46342,0.73716,0.84712,0.59542,0.71595,0.43027,1.25482,0.58628,0.78296,0.0007327,0.0007327,0.0007327
56,51158.6,1.04601,0.46289,0.73734,0.84656,0.59535,0.71571,0.43038,1.25218,0.58575,0.78287,0.00072775,0.00072775,0.00072775
57,52060.4,1.04521,0.46146,0.73717,0.84609,0.59557,0.71554,0.42979,1.25365,0.58606,0.78291,0.0007228,0.0007228,0.0007228
58,52948.6,1.04108,0.4611,0.73755,0.84617,0.59564,0.71516,0.42974,1.2528,0.58554,0.78277,0.00071785,0.00071785,0.00071785
59,53851.7,1.04144,0.46092,0.73741,0.84688,0.59533,0.7151,0.42948,1.25352,0.58561,0.78288,0.0007129,0.0007129,0.0007129
60,54761.6,1.04046,0.46076,0.73733,0.84669,0.59566,0.71514,0.42974,1.252,0.58475,0.78275,0.00070795,0.00070795,0.00070795
61,55685.7,1.04032,0.45854,0.73725,0.84569,0.59584,0.71467,0.42958,1.2517,0.58468,0.7828,0.000703,0.000703,0.000703
62,56569.8,1.03915,0.45977,0.73723,0.84506,0.5953,0.7138,0.42879,1.25329,0.58518,0.78292,0.00069805,0.00069805,0.00069805
63,57414,1.03748,0.45974,0.73704,0.8443,0.59572,0.71358,0.42875,1.25115,0.58496,0.78288,0.0006931,0.0006931,0.0006931
64,58314.8,1.03718,0.45829,0.73712,0.8441,0.59597,0.71321,0.42898,1.24963,0.58386,0.78284,0.00068815,0.00068815,0.00068815
65,59309.3,1.0336,0.45655,0.73752,0.84417,0.59533,0.71259,0.42889,1.249,0.58449,0.78287,0.0006832,0.0006832,0.0006832
66,60355.6,1.03305,0.45657,0.73699,0.84394,0.59586,0.71247,0.42789,1.25232,0.58524,0.78327,0.00067825,0.00067825,0.00067825
67,61448.1,1.03239,0.45607,0.73649,0.84423,0.59496,0.7122,0.42678,1.25673,0.58648,0.7836,0.0006733,0.0006733,0.0006733
68,62266.1,1.0323,0.4561,0.73743,0.84402,0.59492,0.71213,0.42718,1.25533,0.5863,0.78358,0.00066835,0.00066835,0.00066835
69,63152.2,1.03196,0.45606,0.73684,0.84396,0.59532,0.71293,0.42809,1.25406,0.58613,0.78358,0.0006634,0.0006634,0.0006634
70,64016.7,1.02885,0.45484,0.73678,0.84357,0.59555,0.71263,0.42779,1.25569,0.58589,0.78384,0.00065845,0.00065845,0.00065845
71,64826.5,1.02932,0.45468,0.73624,0.8434,0.59543,0.71237,0.42749,1.25596,0.58556,0.78385,0.0006535,0.0006535,0.0006535
72,65752.7,1.02745,0.45392,0.73635,0.84342,0.59537,0.71274,0.42694,1.25778,0.58593,0.78397,0.00064855,0.00064855,0.00064855
73,66585.7,1.02823,0.45346,0.73643,0.84388,0.59528,0.71267,0.42716,1.25664,0.58552,0.78397,0.0006436,0.0006436,0.0006436
74,67526.8,1.02704,0.45321,0.73652,0.8441,0.59536,0.71279,0.42721,1.25571,0.58557,0.78388,0.00063865,0.00063865,0.00063865
75,68439.5,1.02634,0.45238,0.73673,0.84368,0.59539,0.71238,0.42706,1.25717,0.58527,0.78401,0.0006337,0.0006337,0.0006337
76,69308.4,1.02555,0.45177,0.73677,0.84342,0.59574,0.7123,0.42759,1.25365,0.58387,0.78379,0.00062875,0.00062875,0.00062875
77,70213.2,1.02411,0.45217,0.7366,0.84341,0.59567,0.71208,0.42819,1.25214,0.58318,0.78374,0.0006238,0.0006238,0.0006238
78,71106.2,1.02452,0.45112,0.73668,0.84323,0.59597,0.7122,0.42839,1.25092,0.58284,0.78377,0.00061885,0.00061885,0.00061885
79,71948.2,1.02228,0.4517,0.73634,0.8434,0.59549,0.71218,0.42883,1.25063,0.58309,0.7838,0.0006139,0.0006139,0.0006139
80,72809,1.02226,0.45105,0.73632,0.84323,0.59608,0.71237,0.42921,1.25093,0.58295,0.78379,0.00060895,0.00060895,0.00060895
81,73699.7,1.01907,0.45043,0.73658,0.84288,0.59658,0.71228,0.42898,1.25188,0.58351,0.78389,0.000604,0.000604,0.000604
82,74534,1.01756,0.44863,0.7365,0.84282,0.59687,0.7123,0.42911,1.25165,0.58293,0.78376,0.00059905,0.00059905,0.00059905
83,75299.9,1.01809,0.44852,0.7365,0.84414,0.59672,0.71228,0.42976,1.24945,0.58193,0.7836,0.0005941,0.0005941,0.0005941
84,76174.7,1.01855,0.44886,0.73674,0.84476,0.59668,0.71223,0.43007,1.24982,0.58136,0.78368,0.00058915,0.00058915,0.00058915
85,77116.1,1.01508,0.44744,0.7366,0.8459,0.59618,0.71246,0.43051,1.25009,0.58153,0.78368,0.0005842,0.0005842,0.0005842
86,77958.9,1.01681,0.44807,0.73641,0.84586,0.59595,0.7126,0.43031,1.25119,0.58184,0.78369,0.00057925,0.00057925,0.00057925
87,78808.3,1.01515,0.4474,0.73596,0.84702,0.59584,0.71283,0.43081,1.25023,0.5818,0.7836,0.0005743,0.0005743,0.0005743
88,79585.1,1.01586,0.44747,0.73669,0.8478,0.59595,0.71281,0.43096,1.24937,0.58146,0.78363,0.00056935,0.00056935,0.00056935
89,80465.8,1.0139,0.44605,0.73666,0.84835,0.59569,0.71249,0.4305,1.24943,0.58092,0.78359,0.0005644,0.0005644,0.0005644
90,81323.6,1.0129,0.44773,0.73644,0.84768,0.59594,0.71238,0.43068,1.24929,0.58044,0.78349,0.00055945,0.00055945,0.00055945
91,82215.9,1.01239,0.44594,0.73642,0.84843,0.59547,0.71236,0.43089,1.24829,0.57989,0.78341,0.0005545,0.0005545,0.0005545
92,83222.6,1.01113,0.44485,0.73649,0.84812,0.59581,0.71223,0.43108,1.24659,0.57961,0.78332,0.00054955,0.00054955,0.00054955
93,84148.1,1.0117,0.4459,0.73583,0.8483,0.59602,0.7128,0.43193,1.24833,0.57978,0.78338,0.0005446,0.0005446,0.0005446
94,84982.5,1.01133,0.44578,0.73597,0.84877,0.59574,0.71288,0.4319,1.24898,0.57997,0.78336,0.00053965,0.00053965,0.00053965
95,85837.6,1.0102,0.44423,0.73598,0.84997,0.59581,0.71347,0.43281,1.24806,0.57973,0.78346,0.0005347,0.0005347,0.0005347
96,86712.6,1.00786,0.44329,0.73634,0.85032,0.59589,0.71382,0.43264,1.24666,0.579,0.78353,0.00052975,0.00052975,0.00052975
97,87527.7,1.00819,0.44409,0.7361,0.85034,0.59657,0.71429,0.4337,1.24796,0.5787,0.78375,0.0005248,0.0005248,0.0005248
98,88410.7,1.00721,0.44364,0.73598,0.85114,0.59654,0.71453,0.43436,1.24578,0.57753,0.78367,0.00051985,0.00051985,0.00051985
99,89268.2,1.00668,0.44232,0.73614,0.8514,0.59622,0.71457,0.43365,1.24572,0.57727,0.78366,0.0005149,0.0005149,0.0005149
100,90166.9,1.006,0.44285,0.73568,0.85077,0.59689,0.71504,0.43387,1.24547,0.57699,0.7837,0.00050995,0.00050995,0.00050995
101,91030.7,1.00632,0.44333,0.73614,0.85074,0.59667,0.71478,0.43345,1.24594,0.57658,0.78379,0.000505,0.000505,0.000505
102,91898.6,1.0049,0.4419,0.73609,0.85025,0.59671,0.71469,0.43323,1.24495,0.57607,0.7839,0.00050005,0.00050005,0.00050005
103,92757.1,1.00389,0.44232,0.73601,0.85002,0.5976,0.71476,0.43376,1.24281,0.57503,0.78374,0.0004951,0.0004951,0.0004951
104,93647.1,1.00465,0.44161,0.73614,0.85042,0.59716,0.71433,0.43331,1.24269,0.57479,0.78374,0.00049015,0.00049015,0.00049015
105,94524.6,1.00305,0.44075,0.7366,0.85021,0.59754,0.71437,0.43329,1.24264,0.57482,0.78376,0.0004852,0.0004852,0.0004852
106,95413,1.00292,0.44186,0.73555,0.8507,0.59749,0.71458,0.43353,1.24281,0.57477,0.78373,0.00048025,0.00048025,0.00048025
107,96326,1.00177,0.44003,0.73648,0.85025,0.59773,0.71431,0.43323,1.24327,0.57494,0.78371,0.0004753,0.0004753,0.0004753
108,97200.5,1.00182,0.43963,0.73618,0.85076,0.59763,0.7141,0.43336,1.24347,0.57502,0.78377,0.00047035,0.00047035,0.00047035
109,98112.8,1.00165,0.44068,0.73598,0.85159,0.59773,0.71455,0.43388,1.24267,0.57484,0.78378,0.0004654,0.0004654,0.0004654
110,98990.2,1.00146,0.44026,0.73564,0.85225,0.59762,0.71468,0.4351,1.24285,0.57539,0.78399,0.00046045,0.00046045,0.00046045
111,99878.2,0.99901,0.43873,0.73619,0.85258,0.59716,0.71487,0.43525,1.24322,0.57538,0.78399,0.0004555,0.0004555,0.0004555
112,100751,1.00021,0.43983,0.73559,0.85277,0.59677,0.71473,0.43536,1.24265,0.57574,0.78401,0.00045055,0.00045055,0.00045055
113,101666,0.99945,0.44007,0.73567,0.85204,0.59666,0.71494,0.43523,1.24406,0.57614,0.78416,0.0004456,0.0004456,0.0004456
114,102576,0.99743,0.43739,0.73573,0.85159,0.59683,0.71524,0.43559,1.24502,0.57655,0.78437,0.00044065,0.00044065,0.00044065
115,103491,0.99598,0.43738,0.73575,0.85145,0.59645,0.71509,0.43535,1.24706,0.57778,0.78454,0.0004357,0.0004357,0.0004357
116,104350,0.99795,0.43911,0.73581,0.85105,0.59644,0.71533,0.43573,1.2467,0.57804,0.78447,0.00043075,0.00043075,0.00043075
117,105219,0.99677,0.43726,0.73531,0.85049,0.59603,0.71472,0.43488,1.24952,0.57886,0.78466,0.0004258,0.0004258,0.0004258
118,106054,0.9939,0.43704,0.73616,0.85085,0.59601,0.71475,0.43429,1.24998,0.57921,0.78475,0.00042085,0.00042085,0.00042085
119,106906,0.99571,0.43731,0.73575,0.8514,0.59575,0.71476,0.43421,1.25146,0.57974,0.7849,0.0004159,0.0004159,0.0004159
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 723.844 2.07408 2.21817 0.8098 0.71536 0.48978 0.53151 0.25262 1.73505 0.9444 0.81842 0.0670278 0.000333053 0.000333053
3 2 1435.55 1.58293 0.74137 0.75875 0.79959 0.52345 0.59289 0.31563 1.52597 0.81303 0.79943 0.0340245 0.000663087 0.000663087
4 3 2176.86 1.46606 0.67807 0.75444 0.7983 0.51441 0.60086 0.31887 1.55418 0.84581 0.80237 0.00101788 0.000989822 0.000989822
5 4 2966.54 1.38407 0.63657 0.75046 0.80813 0.53779 0.62884 0.33972 1.4425 0.71651 0.79698 0.00098515 0.00098515 0.00098515
6 5 3858.8 1.31864 0.60203 0.74793 0.82935 0.55475 0.64409 0.35485 1.41393 0.69283 0.79108 0.0009802 0.0009802 0.0009802
7 6 4726.74 1.27895 0.58078 0.74608 0.85718 0.561 0.67905 0.39662 1.32381 0.66201 0.78561 0.00097525 0.00097525 0.00097525
8 7 5601.21 1.25517 0.56979 0.74525 0.83164 0.57179 0.6738 0.38316 1.35157 0.64819 0.78647 0.0009703 0.0009703 0.0009703
9 8 6485.42 1.23291 0.55671 0.74456 0.84298 0.56348 0.66817 0.38618 1.31 0.64487 0.78634 0.00096535 0.00096535 0.00096535
10 9 7453.91 1.21657 0.54911 0.74341 0.84957 0.57325 0.68451 0.39491 1.31351 0.63027 0.7854 0.0009604 0.0009604 0.0009604
11 10 8361.51 1.20022 0.54134 0.74261 0.84781 0.57741 0.68773 0.40126 1.29022 0.61634 0.78321 0.00095545 0.00095545 0.00095545
12 11 9240.95 1.18858 0.53458 0.74274 0.83864 0.57749 0.68667 0.40412 1.27972 0.61789 0.78315 0.0009505 0.0009505 0.0009505
13 12 10161.6 1.1797 0.52989 0.74227 0.8378 0.57844 0.68827 0.39961 1.30056 0.61911 0.78465 0.00094555 0.00094555 0.00094555
14 13 11092.6 1.16883 0.52609 0.74147 0.83417 0.57881 0.68672 0.40123 1.29338 0.61691 0.78423 0.0009406 0.0009406 0.0009406
15 14 12068 1.16096 0.52096 0.74113 0.83328 0.58014 0.68871 0.40368 1.28619 0.61376 0.7837 0.00093565 0.00093565 0.00093565
16 15 13026.6 1.15413 0.51717 0.74075 0.83333 0.58048 0.68958 0.40327 1.29169 0.61364 0.78402 0.0009307 0.0009307 0.0009307
17 16 13913.5 1.14571 0.51344 0.74134 0.83365 0.58055 0.69056 0.40359 1.29405 0.61359 0.78406 0.00092575 0.00092575 0.00092575
18 17 14779.3 1.14068 0.51065 0.74023 0.83525 0.5819 0.69303 0.4057 1.29086 0.61156 0.78377 0.0009208 0.0009208 0.0009208
19 18 15716.1 1.13238 0.50791 0.74038 0.83657 0.58256 0.69503 0.4071 1.28866 0.60962 0.78357 0.00091585 0.00091585 0.00091585
20 19 16629.1 1.12878 0.5051 0.74076 0.83729 0.58302 0.69698 0.40788 1.29062 0.60915 0.78367 0.0009109 0.0009109 0.0009109
21 20 17521.1 1.12488 0.50285 0.7399 0.83782 0.58433 0.69918 0.41006 1.28735 0.60737 0.78341 0.00090595 0.00090595 0.00090595
22 21 18492.9 1.11971 0.50015 0.74021 0.83857 0.5858 0.70192 0.41283 1.2834 0.60603 0.7831 0.000901 0.000901 0.000901
23 22 19399.6 1.11692 0.49908 0.73945 0.83934 0.58661 0.70345 0.41335 1.28588 0.6061 0.78323 0.00089605 0.00089605 0.00089605
24 23 20294.5 1.11215 0.49759 0.73928 0.83927 0.58738 0.70438 0.41399 1.28643 0.60604 0.78324 0.0008911 0.0008911 0.0008911
25 24 21151.7 1.10788 0.49421 0.73984 0.84037 0.58837 0.7059 0.41548 1.28522 0.60566 0.78319 0.00088615 0.00088615 0.00088615
26 25 22088.9 1.10476 0.49299 0.73944 0.84095 0.58922 0.70722 0.41772 1.28132 0.60488 0.78295 0.0008812 0.0008812 0.0008812
27 26 23013.5 1.10147 0.4912 0.73954 0.84264 0.58976 0.7089 0.41965 1.27934 0.60437 0.78281 0.00087625 0.00087625 0.00087625
28 27 23959.5 1.09802 0.48966 0.73893 0.84349 0.59073 0.71016 0.42063 1.27834 0.60363 0.78281 0.0008713 0.0008713 0.0008713
29 28 24847.5 1.0993 0.49022 0.7391 0.84557 0.59096 0.71174 0.42268 1.27449 0.60229 0.78259 0.00086635 0.00086635 0.00086635
30 29 25860.6 1.093 0.48708 0.73963 0.84576 0.59192 0.71262 0.4233 1.27515 0.60178 0.7827 0.0008614 0.0008614 0.0008614
31 30 26820.9 1.09207 0.48677 0.73836 0.84635 0.59256 0.71374 0.42469 1.27218 0.6003 0.78259 0.00085645 0.00085645 0.00085645
32 31 27827.5 1.08848 0.4848 0.73905 0.84662 0.59335 0.71493 0.42536 1.27182 0.5997 0.78257 0.0008515 0.0008515 0.0008515
33 32 28795.5 1.08572 0.48334 0.73856 0.84686 0.59368 0.71559 0.42569 1.27282 0.59917 0.78265 0.00084655 0.00084655 0.00084655
34 33 29724.5 1.08286 0.48141 0.73857 0.848 0.59357 0.71659 0.427 1.27097 0.59897 0.7826 0.0008416 0.0008416 0.0008416
35 34 30631.6 1.07959 0.47956 0.73879 0.84889 0.59343 0.71705 0.4274 1.27074 0.59846 0.78264 0.00083665 0.00083665 0.00083665
36 35 31589 1.07928 0.47946 0.73861 0.85032 0.59362 0.71804 0.42825 1.26937 0.59744 0.78259 0.0008317 0.0008317 0.0008317
37 36 32533.2 1.0764 0.47765 0.73841 0.85041 0.5939 0.71861 0.4288 1.26698 0.59606 0.78256 0.00082675 0.00082675 0.00082675
38 37 33532.7 1.07279 0.47748 0.73848 0.85122 0.59396 0.71889 0.4286 1.26801 0.59593 0.78261 0.0008218 0.0008218 0.0008218
39 38 34501.6 1.07425 0.47674 0.73852 0.85176 0.59404 0.71884 0.42911 1.26599 0.59374 0.78256 0.00081685 0.00081685 0.00081685
40 39 35373.3 1.06917 0.47443 0.73859 0.85126 0.5943 0.71847 0.42856 1.2662 0.59232 0.78254 0.0008119 0.0008119 0.0008119
41 40 36284.4 1.06731 0.47499 0.73819 0.85083 0.59518 0.71814 0.42813 1.26535 0.59079 0.78245 0.00080695 0.00080695 0.00080695
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custom_LP_detect/lp_detect/epo_200_frac_0_22/args.yaml
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task: detect
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89
custom_LP_detect/lp_detect/epo_200_frac_0_22/results.csv
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27,24015.5,1.10799,0.49545,0.73951,0.85661,0.5887,0.70653,0.41796,1.28199,0.59877,0.78289,0.0008713,0.0008713,0.0008713
28,24977.7,1.10307,0.49255,0.73928,0.85697,0.5894,0.70756,0.41889,1.28116,0.5989,0.78294,0.00086635,0.00086635,0.00086635
29,25896.4,1.10171,0.49035,0.7399,0.8573,0.58964,0.70884,0.41901,1.28514,0.60023,0.78318,0.0008614,0.0008614,0.0008614
30,26884.4,1.10104,0.49111,0.7392,0.85768,0.59027,0.71004,0.41939,1.28589,0.59916,0.78324,0.00085645,0.00085645,0.00085645
31,27821.5,1.09736,0.48862,0.73946,0.85746,0.59113,0.71097,0.42024,1.28557,0.59891,0.78314,0.0008515,0.0008515,0.0008515
32,28733.1,1.09599,0.48775,0.73911,0.85809,0.59126,0.71157,0.42121,1.28466,0.598,0.78303,0.00084655,0.00084655,0.00084655
33,29706,1.09393,0.48622,0.73889,0.85828,0.59124,0.71156,0.42122,1.2855,0.59761,0.78301,0.0008416,0.0008416,0.0008416
34,30760.6,1.09213,0.48571,0.73892,0.85874,0.59141,0.71226,0.4217,1.28475,0.59705,0.78299,0.00083665,0.00083665,0.00083665
35,31740.2,1.08675,0.48405,0.73861,0.85966,0.59144,0.71291,0.42296,1.2837,0.59656,0.78303,0.0008317,0.0008317,0.0008317
36,32605.9,1.08581,0.48359,0.73853,0.85936,0.59196,0.71396,0.42344,1.28431,0.59652,0.78307,0.00082675,0.00082675,0.00082675
37,33541.1,1.0835,0.48112,0.73894,0.85966,0.5924,0.71531,0.42436,1.28276,0.59629,0.78294,0.0008218,0.0008218,0.0008218
38,34450.5,1.07874,0.48046,0.73806,0.85953,0.59261,0.71569,0.42513,1.27985,0.59526,0.78279,0.00081685,0.00081685,0.00081685
39,35388.2,1.07805,0.47898,0.73868,0.86018,0.59257,0.71596,0.4264,1.27597,0.59333,0.78266,0.0008119,0.0008119,0.0008119
40,36385.5,1.07741,0.47892,0.73895,0.85977,0.59289,0.71615,0.42643,1.27493,0.59258,0.78264,0.00080695,0.00080695,0.00080695
41,37396.2,1.07492,0.47629,0.73844,0.85985,0.59282,0.71633,0.42756,1.27334,0.59238,0.7827,0.000802,0.000802,0.000802
42,38336.4,1.07379,0.47665,0.73838,0.86076,0.59259,0.71672,0.42822,1.27189,0.59201,0.78263,0.00079705,0.00079705,0.00079705
43,39303.9,1.07174,0.47517,0.7387,0.86162,0.59276,0.71719,0.42859,1.27001,0.59113,0.78267,0.0007921,0.0007921,0.0007921
44,40324.3,1.07322,0.4763,0.73838,0.86297,0.59271,0.71744,0.42976,1.26764,0.59017,0.78261,0.00078715,0.00078715,0.00078715
45,41422.2,1.06913,0.47481,0.7379,0.86292,0.59329,0.7179,0.43054,1.2653,0.58916,0.78264,0.0007822,0.0007822,0.0007822
46,42420.1,1.06649,0.47469,0.73819,0.8625,0.59358,0.71816,0.43129,1.26336,0.58825,0.78259,0.00077725,0.00077725,0.00077725
47,43397.9,1.06743,0.47507,0.73795,0.86227,0.59341,0.71781,0.43132,1.26294,0.58816,0.78257,0.0007723,0.0007723,0.0007723
48,44368.4,1.06518,0.47236,0.73805,0.86187,0.59335,0.7178,0.43179,1.26079,0.58801,0.78245,0.00076735,0.00076735,0.00076735
49,45305.6,1.06516,0.47263,0.73795,0.86119,0.59339,0.7173,0.43038,1.26234,0.5882,0.78253,0.0007624,0.0007624,0.0007624
50,46243.3,1.06248,0.47116,0.73813,0.86201,0.59264,0.71756,0.4314,1.25966,0.58834,0.78241,0.00075745,0.00075745,0.00075745
51,47137.6,1.06006,0.47007,0.73811,0.86184,0.5926,0.71724,0.43097,1.25968,0.58829,0.78252,0.0007525,0.0007525,0.0007525
52,48148.9,1.0573,0.46921,0.7378,0.86197,0.59251,0.71712,0.43023,1.26029,0.5883,0.78264,0.00074755,0.00074755,0.00074755
53,49047.6,1.05948,0.46972,0.73794,0.86222,0.59246,0.71669,0.4288,1.26147,0.58763,0.78275,0.0007426,0.0007426,0.0007426
54,50003.7,1.05605,0.46839,0.73764,0.86138,0.59297,0.71709,0.42863,1.26179,0.58758,0.78277,0.00073765,0.00073765,0.00073765
55,50920.2,1.05369,0.46685,0.73762,0.86218,0.59249,0.71732,0.42842,1.26457,0.58879,0.78306,0.0007327,0.0007327,0.0007327
56,51819.8,1.05221,0.46597,0.73756,0.86184,0.59248,0.71718,0.42826,1.26547,0.58888,0.78324,0.00072775,0.00072775,0.00072775
57,52778.7,1.05184,0.46607,0.73766,0.86219,0.59243,0.71702,0.42813,1.26579,0.58908,0.78327,0.0007228,0.0007228,0.0007228
58,53745.3,1.05082,0.46603,0.73738,0.86305,0.59192,0.717,0.42843,1.26523,0.58902,0.78331,0.00071785,0.00071785,0.00071785
59,54734.8,1.04964,0.46449,0.73742,0.86359,0.59146,0.71713,0.42905,1.26538,0.58903,0.7835,0.0007129,0.0007129,0.0007129
60,55744.6,1.04881,0.4646,0.73761,0.86311,0.59145,0.71715,0.42874,1.26711,0.58921,0.78361,0.00070795,0.00070795,0.00070795
61,56712.6,1.04673,0.46278,0.73797,0.86292,0.5914,0.71733,0.42865,1.26763,0.5895,0.78368,0.000703,0.000703,0.000703
62,57635,1.04612,0.46248,0.73763,0.86188,0.59147,0.71735,0.42877,1.26679,0.58916,0.7837,0.00069805,0.00069805,0.00069805
63,58639,1.04511,0.46204,0.73736,0.86158,0.59145,0.71748,0.42837,1.2681,0.58965,0.78382,0.0006931,0.0006931,0.0006931
64,59538.1,1.04695,0.4633,0.7374,0.86094,0.59143,0.71753,0.42772,1.27074,0.59001,0.78404,0.00068815,0.00068815,0.00068815
65,60483.4,1.04596,0.4622,0.73738,0.86003,0.5915,0.71743,0.42744,1.26962,0.5898,0.78398,0.0006832,0.0006832,0.0006832
66,61480.7,1.04433,0.4623,0.73698,0.8599,0.59133,0.71725,0.42697,1.27098,0.59032,0.78409,0.00067825,0.00067825,0.00067825
67,62443.2,1.04174,0.46117,0.7373,0.85955,0.59132,0.71707,0.42668,1.27073,0.59001,0.78415,0.0006733,0.0006733,0.0006733
68,63391.3,1.04008,0.46057,0.73727,0.85926,0.59167,0.7172,0.42672,1.26904,0.58894,0.78412,0.00066835,0.00066835,0.00066835
69,64349.8,1.0404,0.46023,0.73744,0.8588,0.59206,0.71767,0.42681,1.27067,0.58929,0.7843,0.0006634,0.0006634,0.0006634
70,65354.4,1.03638,0.4588,0.73739,0.85895,0.5922,0.71774,0.42677,1.27193,0.58964,0.78437,0.00065845,0.00065845,0.00065845
71,66276.9,1.03584,0.45816,0.73709,0.85869,0.59222,0.71743,0.42658,1.27146,0.5896,0.78442,0.0006535,0.0006535,0.0006535
72,67253.4,1.03837,0.4582,0.73715,0.85864,0.59213,0.71766,0.42662,1.27271,0.58969,0.78456,0.00064855,0.00064855,0.00064855
73,68182.5,1.03438,0.45738,0.73725,0.8596,0.59187,0.71815,0.42681,1.27374,0.58999,0.78458,0.0006436,0.0006436,0.0006436
74,69137,1.03597,0.45786,0.7369,0.86006,0.59241,0.71872,0.42775,1.27188,0.58967,0.78436,0.00063865,0.00063865,0.00063865
75,70081.1,1.03436,0.45652,0.73676,0.86105,0.59253,0.71907,0.42807,1.27283,0.58996,0.78442,0.0006337,0.0006337,0.0006337
76,71137.3,1.03396,0.45593,0.73722,0.86188,0.59273,0.71947,0.42855,1.27351,0.59011,0.7844,0.00062875,0.00062875,0.00062875
77,72138.3,1.03367,0.45615,0.73681,0.86171,0.59299,0.71939,0.42833,1.27501,0.59028,0.78455,0.0006238,0.0006238,0.0006238
78,73135.8,1.03212,0.45593,0.73677,0.86149,0.59285,0.71918,0.42722,1.27964,0.59159,0.78491,0.00061885,0.00061885,0.00061885
79,74122.1,1.0303,0.45532,0.73731,0.86198,0.59288,0.71911,0.42744,1.27849,0.59115,0.78488,0.0006139,0.0006139,0.0006139
80,75098.2,1.03031,0.45587,0.73735,0.86241,0.59323,0.71956,0.4282,1.2752,0.59024,0.78469,0.00060895,0.00060895,0.00060895
81,76067.1,1.02756,0.45412,0.73639,0.86239,0.59284,0.71939,0.42786,1.27612,0.59031,0.78477,0.000604,0.000604,0.000604
82,77026.9,1.02814,0.4541,0.73714,0.86166,0.5932,0.7196,0.42797,1.27736,0.59061,0.78491,0.00059905,0.00059905,0.00059905
83,78037.1,1.02686,0.45258,0.73663,0.86208,0.59277,0.71978,0.42826,1.27723,0.59115,0.78496,0.0005941,0.0005941,0.0005941
84,78997.6,1.02763,0.45326,0.73653,0.86215,0.59249,0.71995,0.42836,1.27747,0.59164,0.78494,0.00058915,0.00058915,0.00058915
85,79925.5,1.02697,0.45207,0.73666,0.8622,0.59221,0.71984,0.4283,1.27819,0.59187,0.78512,0.0005842,0.0005842,0.0005842
86,80886.2,1.02473,0.45253,0.73638,0.86175,0.59258,0.72004,0.42852,1.27797,0.59178,0.78518,0.00057925,0.00057925,0.00057925
87,81811.8,1.02297,0.4501,0.73671,0.86142,0.59267,0.72022,0.42861,1.27827,0.59236,0.78528,0.0005743,0.0005743,0.0005743
88,82914,1.02263,0.451,0.73627,0.86116,0.59264,0.72057,0.42855,1.28018,0.59284,0.78539,0.00056935,0.00056935,0.00056935
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 670.082 2.08443 2.44821 0.81081 0.71851 0.47457 0.52971 0.23364 1.83755 0.95405 0.8215 0.0670278 0.000333053 0.000333053
3 2 1313.75 1.58716 0.74414 0.75968 0.81677 0.52719 0.61301 0.3289 1.5445 0.85338 0.8027 0.0340245 0.000663087 0.000663087
4 3 2135 1.47432 0.68417 0.75414 0.80856 0.53261 0.6084 0.32633 1.42827 0.7321 0.79235 0.00101788 0.000989822 0.000989822
5 4 2940.55 1.38928 0.63927 0.75077 0.83619 0.53635 0.64671 0.36316 1.38902 0.72055 0.78939 0.00098515 0.00098515 0.00098515
6 5 3817.13 1.32709 0.60788 0.74776 0.84566 0.56372 0.67415 0.39056 1.33281 0.67538 0.78496 0.0009802 0.0009802 0.0009802
7 6 4712.28 1.28936 0.5872 0.74673 0.84266 0.57438 0.68033 0.40443 1.27209 0.64319 0.78345 0.00097525 0.00097525 0.00097525
8 7 5605.85 1.25984 0.5727 0.74524 0.84782 0.57 0.67373 0.38835 1.31188 0.63767 0.78474 0.0009703 0.0009703 0.0009703
9 8 6478.77 1.23884 0.56163 0.74487 0.84269 0.57909 0.68077 0.39161 1.30807 0.631 0.78444 0.00096535 0.00096535 0.00096535
10 9 7355.57 1.22429 0.55468 0.74353 0.84271 0.58262 0.68476 0.3964 1.3083 0.62192 0.78313 0.0009604 0.0009604 0.0009604
11 10 8248.26 1.20836 0.5464 0.74268 0.85332 0.58195 0.6895 0.40035 1.29491 0.60687 0.78301 0.00095545 0.00095545 0.00095545
12 11 9160.01 1.19649 0.54082 0.74272 0.84809 0.58179 0.68902 0.4039 1.28383 0.60786 0.78289 0.0009505 0.0009505 0.0009505
13 12 10080.5 1.18664 0.5344 0.74255 0.84878 0.582 0.6914 0.4099 1.26084 0.60217 0.78158 0.00094555 0.00094555 0.00094555
14 13 11021.8 1.17938 0.5308 0.74207 0.84738 0.58431 0.69285 0.40928 1.26915 0.60065 0.78187 0.0009406 0.0009406 0.0009406
15 14 11957.6 1.16793 0.52703 0.74182 0.85144 0.58354 0.692 0.40857 1.27586 0.59912 0.78243 0.00093565 0.00093565 0.00093565
16 15 12879.7 1.16249 0.52237 0.74152 0.85196 0.58473 0.69246 0.41108 1.26865 0.59603 0.78204 0.0009307 0.0009307 0.0009307
17 16 13850.6 1.15473 0.51898 0.74159 0.85152 0.5851 0.69312 0.41148 1.27044 0.59652 0.78222 0.00092575 0.00092575 0.00092575
18 17 14760.6 1.14727 0.51403 0.74096 0.85174 0.58578 0.69456 0.41141 1.27299 0.59684 0.78234 0.0009208 0.0009208 0.0009208
19 18 15688.3 1.14333 0.51291 0.7403 0.85175 0.58622 0.69548 0.41184 1.27237 0.59619 0.7823 0.00091585 0.00091585 0.00091585
20 19 16656.6 1.13888 0.51063 0.74074 0.85228 0.58636 0.69611 0.41199 1.27203 0.59565 0.78233 0.0009109 0.0009109 0.0009109
21 20 17547.1 1.13396 0.50832 0.74022 0.85282 0.58659 0.69722 0.41239 1.2737 0.59554 0.78244 0.00090595 0.00090595 0.00090595
22 21 18501 1.13001 0.50554 0.74079 0.85329 0.58701 0.6987 0.41363 1.27317 0.59504 0.78238 0.000901 0.000901 0.000901
23 22 19360 1.12565 0.50435 0.7398 0.85328 0.58751 0.69963 0.41424 1.2746 0.59537 0.78246 0.00089605 0.00089605 0.00089605
24 23 20292.4 1.11877 0.50094 0.74014 0.8547 0.58744 0.7011 0.41513 1.27621 0.5957 0.78251 0.0008911 0.0008911 0.0008911
25 24 21207.3 1.1184 0.49949 0.73969 0.85441 0.58778 0.70225 0.41558 1.27726 0.59622 0.78256 0.00088615 0.00088615 0.00088615
26 25 22166.8 1.11235 0.49746 0.74001 0.855 0.58808 0.70329 0.41627 1.27749 0.59641 0.78255 0.0008812 0.0008812 0.0008812
27 26 23091.2 1.11046 0.49556 0.73924 0.85534 0.58834 0.70466 0.41665 1.28011 0.5978 0.7827 0.00087625 0.00087625 0.00087625
28 27 24015.5 1.10799 0.49545 0.73951 0.85661 0.5887 0.70653 0.41796 1.28199 0.59877 0.78289 0.0008713 0.0008713 0.0008713
29 28 24977.7 1.10307 0.49255 0.73928 0.85697 0.5894 0.70756 0.41889 1.28116 0.5989 0.78294 0.00086635 0.00086635 0.00086635
30 29 25896.4 1.10171 0.49035 0.7399 0.8573 0.58964 0.70884 0.41901 1.28514 0.60023 0.78318 0.0008614 0.0008614 0.0008614
31 30 26884.4 1.10104 0.49111 0.7392 0.85768 0.59027 0.71004 0.41939 1.28589 0.59916 0.78324 0.00085645 0.00085645 0.00085645
32 31 27821.5 1.09736 0.48862 0.73946 0.85746 0.59113 0.71097 0.42024 1.28557 0.59891 0.78314 0.0008515 0.0008515 0.0008515
33 32 28733.1 1.09599 0.48775 0.73911 0.85809 0.59126 0.71157 0.42121 1.28466 0.598 0.78303 0.00084655 0.00084655 0.00084655
34 33 29706 1.09393 0.48622 0.73889 0.85828 0.59124 0.71156 0.42122 1.2855 0.59761 0.78301 0.0008416 0.0008416 0.0008416
35 34 30760.6 1.09213 0.48571 0.73892 0.85874 0.59141 0.71226 0.4217 1.28475 0.59705 0.78299 0.00083665 0.00083665 0.00083665
36 35 31740.2 1.08675 0.48405 0.73861 0.85966 0.59144 0.71291 0.42296 1.2837 0.59656 0.78303 0.0008317 0.0008317 0.0008317
37 36 32605.9 1.08581 0.48359 0.73853 0.85936 0.59196 0.71396 0.42344 1.28431 0.59652 0.78307 0.00082675 0.00082675 0.00082675
38 37 33541.1 1.0835 0.48112 0.73894 0.85966 0.5924 0.71531 0.42436 1.28276 0.59629 0.78294 0.0008218 0.0008218 0.0008218
39 38 34450.5 1.07874 0.48046 0.73806 0.85953 0.59261 0.71569 0.42513 1.27985 0.59526 0.78279 0.00081685 0.00081685 0.00081685
40 39 35388.2 1.07805 0.47898 0.73868 0.86018 0.59257 0.71596 0.4264 1.27597 0.59333 0.78266 0.0008119 0.0008119 0.0008119
41 40 36385.5 1.07741 0.47892 0.73895 0.85977 0.59289 0.71615 0.42643 1.27493 0.59258 0.78264 0.00080695 0.00080695 0.00080695
42 41 37396.2 1.07492 0.47629 0.73844 0.85985 0.59282 0.71633 0.42756 1.27334 0.59238 0.7827 0.000802 0.000802 0.000802
43 42 38336.4 1.07379 0.47665 0.73838 0.86076 0.59259 0.71672 0.42822 1.27189 0.59201 0.78263 0.00079705 0.00079705 0.00079705
44 43 39303.9 1.07174 0.47517 0.7387 0.86162 0.59276 0.71719 0.42859 1.27001 0.59113 0.78267 0.0007921 0.0007921 0.0007921
45 44 40324.3 1.07322 0.4763 0.73838 0.86297 0.59271 0.71744 0.42976 1.26764 0.59017 0.78261 0.00078715 0.00078715 0.00078715
46 45 41422.2 1.06913 0.47481 0.7379 0.86292 0.59329 0.7179 0.43054 1.2653 0.58916 0.78264 0.0007822 0.0007822 0.0007822
47 46 42420.1 1.06649 0.47469 0.73819 0.8625 0.59358 0.71816 0.43129 1.26336 0.58825 0.78259 0.00077725 0.00077725 0.00077725
48 47 43397.9 1.06743 0.47507 0.73795 0.86227 0.59341 0.71781 0.43132 1.26294 0.58816 0.78257 0.0007723 0.0007723 0.0007723
49 48 44368.4 1.06518 0.47236 0.73805 0.86187 0.59335 0.7178 0.43179 1.26079 0.58801 0.78245 0.00076735 0.00076735 0.00076735
50 49 45305.6 1.06516 0.47263 0.73795 0.86119 0.59339 0.7173 0.43038 1.26234 0.5882 0.78253 0.0007624 0.0007624 0.0007624
51 50 46243.3 1.06248 0.47116 0.73813 0.86201 0.59264 0.71756 0.4314 1.25966 0.58834 0.78241 0.00075745 0.00075745 0.00075745
52 51 47137.6 1.06006 0.47007 0.73811 0.86184 0.5926 0.71724 0.43097 1.25968 0.58829 0.78252 0.0007525 0.0007525 0.0007525
53 52 48148.9 1.0573 0.46921 0.7378 0.86197 0.59251 0.71712 0.43023 1.26029 0.5883 0.78264 0.00074755 0.00074755 0.00074755
54 53 49047.6 1.05948 0.46972 0.73794 0.86222 0.59246 0.71669 0.4288 1.26147 0.58763 0.78275 0.0007426 0.0007426 0.0007426
55 54 50003.7 1.05605 0.46839 0.73764 0.86138 0.59297 0.71709 0.42863 1.26179 0.58758 0.78277 0.00073765 0.00073765 0.00073765
56 55 50920.2 1.05369 0.46685 0.73762 0.86218 0.59249 0.71732 0.42842 1.26457 0.58879 0.78306 0.0007327 0.0007327 0.0007327
57 56 51819.8 1.05221 0.46597 0.73756 0.86184 0.59248 0.71718 0.42826 1.26547 0.58888 0.78324 0.00072775 0.00072775 0.00072775
58 57 52778.7 1.05184 0.46607 0.73766 0.86219 0.59243 0.71702 0.42813 1.26579 0.58908 0.78327 0.0007228 0.0007228 0.0007228
59 58 53745.3 1.05082 0.46603 0.73738 0.86305 0.59192 0.717 0.42843 1.26523 0.58902 0.78331 0.00071785 0.00071785 0.00071785
60 59 54734.8 1.04964 0.46449 0.73742 0.86359 0.59146 0.71713 0.42905 1.26538 0.58903 0.7835 0.0007129 0.0007129 0.0007129
61 60 55744.6 1.04881 0.4646 0.73761 0.86311 0.59145 0.71715 0.42874 1.26711 0.58921 0.78361 0.00070795 0.00070795 0.00070795
62 61 56712.6 1.04673 0.46278 0.73797 0.86292 0.5914 0.71733 0.42865 1.26763 0.5895 0.78368 0.000703 0.000703 0.000703
63 62 57635 1.04612 0.46248 0.73763 0.86188 0.59147 0.71735 0.42877 1.26679 0.58916 0.7837 0.00069805 0.00069805 0.00069805
64 63 58639 1.04511 0.46204 0.73736 0.86158 0.59145 0.71748 0.42837 1.2681 0.58965 0.78382 0.0006931 0.0006931 0.0006931
65 64 59538.1 1.04695 0.4633 0.7374 0.86094 0.59143 0.71753 0.42772 1.27074 0.59001 0.78404 0.00068815 0.00068815 0.00068815
66 65 60483.4 1.04596 0.4622 0.73738 0.86003 0.5915 0.71743 0.42744 1.26962 0.5898 0.78398 0.0006832 0.0006832 0.0006832
67 66 61480.7 1.04433 0.4623 0.73698 0.8599 0.59133 0.71725 0.42697 1.27098 0.59032 0.78409 0.00067825 0.00067825 0.00067825
68 67 62443.2 1.04174 0.46117 0.7373 0.85955 0.59132 0.71707 0.42668 1.27073 0.59001 0.78415 0.0006733 0.0006733 0.0006733
69 68 63391.3 1.04008 0.46057 0.73727 0.85926 0.59167 0.7172 0.42672 1.26904 0.58894 0.78412 0.00066835 0.00066835 0.00066835
70 69 64349.8 1.0404 0.46023 0.73744 0.8588 0.59206 0.71767 0.42681 1.27067 0.58929 0.7843 0.0006634 0.0006634 0.0006634
71 70 65354.4 1.03638 0.4588 0.73739 0.85895 0.5922 0.71774 0.42677 1.27193 0.58964 0.78437 0.00065845 0.00065845 0.00065845
72 71 66276.9 1.03584 0.45816 0.73709 0.85869 0.59222 0.71743 0.42658 1.27146 0.5896 0.78442 0.0006535 0.0006535 0.0006535
73 72 67253.4 1.03837 0.4582 0.73715 0.85864 0.59213 0.71766 0.42662 1.27271 0.58969 0.78456 0.00064855 0.00064855 0.00064855
74 73 68182.5 1.03438 0.45738 0.73725 0.8596 0.59187 0.71815 0.42681 1.27374 0.58999 0.78458 0.0006436 0.0006436 0.0006436
75 74 69137 1.03597 0.45786 0.7369 0.86006 0.59241 0.71872 0.42775 1.27188 0.58967 0.78436 0.00063865 0.00063865 0.00063865
76 75 70081.1 1.03436 0.45652 0.73676 0.86105 0.59253 0.71907 0.42807 1.27283 0.58996 0.78442 0.0006337 0.0006337 0.0006337
77 76 71137.3 1.03396 0.45593 0.73722 0.86188 0.59273 0.71947 0.42855 1.27351 0.59011 0.7844 0.00062875 0.00062875 0.00062875
78 77 72138.3 1.03367 0.45615 0.73681 0.86171 0.59299 0.71939 0.42833 1.27501 0.59028 0.78455 0.0006238 0.0006238 0.0006238
79 78 73135.8 1.03212 0.45593 0.73677 0.86149 0.59285 0.71918 0.42722 1.27964 0.59159 0.78491 0.00061885 0.00061885 0.00061885
80 79 74122.1 1.0303 0.45532 0.73731 0.86198 0.59288 0.71911 0.42744 1.27849 0.59115 0.78488 0.0006139 0.0006139 0.0006139
81 80 75098.2 1.03031 0.45587 0.73735 0.86241 0.59323 0.71956 0.4282 1.2752 0.59024 0.78469 0.00060895 0.00060895 0.00060895
82 81 76067.1 1.02756 0.45412 0.73639 0.86239 0.59284 0.71939 0.42786 1.27612 0.59031 0.78477 0.000604 0.000604 0.000604
83 82 77026.9 1.02814 0.4541 0.73714 0.86166 0.5932 0.7196 0.42797 1.27736 0.59061 0.78491 0.00059905 0.00059905 0.00059905
84 83 78037.1 1.02686 0.45258 0.73663 0.86208 0.59277 0.71978 0.42826 1.27723 0.59115 0.78496 0.0005941 0.0005941 0.0005941
85 84 78997.6 1.02763 0.45326 0.73653 0.86215 0.59249 0.71995 0.42836 1.27747 0.59164 0.78494 0.00058915 0.00058915 0.00058915
86 85 79925.5 1.02697 0.45207 0.73666 0.8622 0.59221 0.71984 0.4283 1.27819 0.59187 0.78512 0.0005842 0.0005842 0.0005842
87 86 80886.2 1.02473 0.45253 0.73638 0.86175 0.59258 0.72004 0.42852 1.27797 0.59178 0.78518 0.00057925 0.00057925 0.00057925
88 87 81811.8 1.02297 0.4501 0.73671 0.86142 0.59267 0.72022 0.42861 1.27827 0.59236 0.78528 0.0005743 0.0005743 0.0005743
89 88 82914 1.02263 0.451 0.73627 0.86116 0.59264 0.72057 0.42855 1.28018 0.59284 0.78539 0.00056935 0.00056935 0.00056935

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a7676704",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3e94066a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "99b0442c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New https://pypi.org/project/ultralytics/8.3.228 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/custom_LP_detect/custom_LP.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=200, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=0.2, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=epo_200_frac_0_22, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=40, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=lp_detect, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=1\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
" 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n",
" 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
" 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n",
" 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
" 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
" 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n",
" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
" 22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] \n",
"Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs\n",
"\n",
"Transferred 319/355 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1415.2±186.1 MB/s, size: 1748.6 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/labels/license_plate/ar01_01.cache... 160269 images, 0 backgrounds, 2 corrupt: 100% ━━━━━━━━━━━━ 160271/160271 514.3Mit/s 0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-220930_08_AR01_01_N0023.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221011_13_AR01_01_N4945.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221019_12_AR01_01_N5133.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221019_14_AR01_01_N0089.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar03_03/C-221105_17_AR03_03_N0282.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar03_03/C-221109_14_AR03_03_N0673.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar04_04/C-221022_14_AR04_04_N0674.jpg: 2 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar09_01/C-220807_15_AR09_01_N0514.jpg: ignoring corrupt image/label: image file is truncated (107 bytes not processed)\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/cr01_02/C-220827_16_CR01_02_N0098.jpg: ignoring corrupt image/label: image file is truncated (96 bytes not processed)\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 0.15G reserved, 0.05G allocated, 31.13G free\n",
" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
" 3011043 8.194 3.456 31.09 193.1 (1, 3, 640, 640) list\n",
" 3011043 16.39 3.997 4.507 23.43 (2, 3, 640, 640) list\n",
" 3011043 32.78 4.270 5.17 24.49 (4, 3, 640, 640) list\n",
" 3011043 65.55 4.979 5.493 31.44 (8, 3, 640, 640) list\n",
" 3011043 131.1 6.201 7.781 41.38 (16, 3, 640, 640) list\n",
" 3011043 262.2 5.014 15.01 55.64 (32, 3, 640, 640) list\n",
" 3011043 524.4 10.863 31.73 105.9 (64, 3, 640, 640) list\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 135 for CUDA:0 19.16G/31.33G (61%) ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1182.9±337.0 MB/s, size: 1333.9 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/labels/license_plate/ar01_01.cache... 160269 images, 0 backgrounds, 2 corrupt: 100% ━━━━━━━━━━━━ 160271/160271 317.1Mit/s 0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-220930_08_AR01_01_N0023.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221011_13_AR01_01_N4945.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221019_12_AR01_01_N5133.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar01_01/C-221019_14_AR01_01_N0089.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar03_03/C-221105_17_AR03_03_N0282.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar03_03/C-221109_14_AR03_03_N0673.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar04_04/C-221022_14_AR04_04_N0674.jpg: 2 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/ar09_01/C-220807_15_AR09_01_N0514.jpg: ignoring corrupt image/label: image file is truncated (107 bytes not processed)\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Training/images/license_plate/cr01_02/C-220827_16_CR01_02_N0098.jpg: ignoring corrupt image/label: image file is truncated (96 bytes not processed)\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1014.6±261.7 MB/s, size: 1776.7 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/labels/license_plate/ar01_01.cache... 52168 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 52168/52168 98.1Mit/s 0.0s\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/ar01_01/C-221030_13_AR01_01_N0451.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/sr02_01/C-220921_13_SR02_01_N0664.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/sr08_01/C-221026_17_SR08_01_N0368.jpg: 3 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/sr10_01/C-221111_14_SR10_01_N2786.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/sr10_01/C-221112_10_SR10_01_N3102.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/CCTV_car_or_licenseplate/data/Validation/images/license_plate/sr10_01/C-221112_13_SR10_01_N1269.jpg: 1 duplicate labels removed\n",
"Plotting labels to /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0010546875), 63 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1m/home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22\u001b[0m\n",
"Starting training for 200 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 1/200 18.1G 2.084 2.448 0.8108 125 640: 100% ━━━━━━━━━━━━ 1188/1188 2.1it/s 9:14<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.7sss\n",
" all 52168 159183 0.719 0.475 0.53 0.234\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 2/200 18G 1.587 0.7441 0.7597 115 640: 100% ━━━━━━━━━━━━ 1188/1188 2.2it/s 8:56<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.7sss\n",
" all 52168 159183 0.817 0.527 0.613 0.329\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 3/200 18G 1.474 0.6842 0.7541 108 640: 100% ━━━━━━━━━━━━ 1188/1188 1.7it/s 11:43<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:420.7sss\n",
" all 52168 159183 0.809 0.533 0.608 0.326\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 4/200 18G 1.389 0.6393 0.7508 123 640: 100% ━━━━━━━━━━━━ 1188/1188 1.7it/s 11:40<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:370.6sss\n",
" all 52168 159183 0.836 0.536 0.647 0.363\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 5/200 18G 1.327 0.6079 0.7478 100 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 12:49<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:420.5sss\n",
" all 52168 159183 0.846 0.564 0.674 0.391\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 6/200 18G 1.289 0.5872 0.7467 130 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:20<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.6sss\n",
" all 52168 159183 0.843 0.574 0.68 0.404\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 7/200 18G 1.26 0.5727 0.7452 133 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:09<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.6sss\n",
" all 52168 159183 0.848 0.57 0.674 0.388\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 8/200 18G 1.239 0.5616 0.7449 98 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 12:50<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.7sss\n",
" all 52168 159183 0.843 0.579 0.681 0.392\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 9/200 18G 1.224 0.5547 0.7435 119 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 12:51<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:380.6sss\n",
" all 52168 159183 0.843 0.583 0.685 0.396\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 10/200 18G 1.208 0.5464 0.7427 107 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:12<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.7sss\n",
" all 52168 159183 0.853 0.582 0.689 0.4\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 11/200 18G 1.196 0.5408 0.7427 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:22<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:410.7sss\n",
" all 52168 159183 0.848 0.582 0.689 0.404\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 12/200 18G 1.187 0.5344 0.7425 104 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:36<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:380.6sss\n",
" all 52168 159183 0.849 0.582 0.691 0.41\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 13/200 18G 1.179 0.5308 0.7421 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:55<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:380.7sss\n",
" all 52168 159183 0.847 0.584 0.693 0.409\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 14/200 18G 1.168 0.527 0.7418 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:56<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.6sss\n",
" all 52168 159183 0.851 0.584 0.692 0.409\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 15/200 18G 1.162 0.5224 0.7415 106 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:42<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.5sss\n",
" all 52168 159183 0.852 0.585 0.692 0.411\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 16/200 18G 1.155 0.519 0.7416 125 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:38<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:270.5sss\n",
" all 52168 159183 0.852 0.585 0.693 0.411\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 17/200 18G 1.147 0.514 0.741 106 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:29<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.852 0.586 0.695 0.411\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 18/200 18G 1.143 0.5129 0.7403 147 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:47<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.852 0.586 0.695 0.412\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 19/200 18G 1.139 0.5106 0.7407 135 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:24<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:390.6sss\n",
" all 52168 159183 0.852 0.586 0.696 0.412\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 20/200 18G 1.134 0.5083 0.7402 106 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:08<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.5sss\n",
" all 52168 159183 0.853 0.587 0.697 0.412\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 21/200 18G 1.13 0.5055 0.7408 113 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:10<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.7sss\n",
" all 52168 159183 0.853 0.587 0.699 0.414\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 22/200 18G 1.126 0.5043 0.7398 134 640: 100% ━━━━━━━━━━━━ 1188/1188 1.6it/s 12:41<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.7sss\n",
" all 52168 159183 0.853 0.588 0.7 0.414\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 23/200 18G 1.119 0.5009 0.7401 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:51<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.5sss\n",
" all 52168 159183 0.855 0.587 0.701 0.415\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 24/200 18G 1.118 0.4995 0.7397 120 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:39<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.6sss\n",
" all 52168 159183 0.854 0.588 0.702 0.416\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 25/200 18G 1.112 0.4975 0.74 124 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:17<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.855 0.588 0.703 0.416\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 26/200 18G 1.11 0.4956 0.7392 109 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:45<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.6sss\n",
" all 52168 159183 0.855 0.588 0.705 0.417\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 27/200 18G 1.108 0.4955 0.7395 116 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:42<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.857 0.589 0.707 0.418\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 28/200 18G 1.103 0.4925 0.7393 106 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:22<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.857 0.589 0.708 0.419\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 29/200 18G 1.102 0.4903 0.7399 101 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:36<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:370.6sss\n",
" all 52168 159183 0.857 0.59 0.709 0.419\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 30/200 18G 1.101 0.4911 0.7392 116 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:49<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.6sss\n",
" all 52168 159183 0.858 0.59 0.71 0.419\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 31/200 18G 1.097 0.4886 0.7395 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:54<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.7sss\n",
" all 52168 159183 0.857 0.591 0.711 0.42\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 32/200 18G 1.096 0.4877 0.7391 90 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:41<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:270.5sss\n",
" all 52168 159183 0.858 0.591 0.712 0.421\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 33/200 18G 1.094 0.4862 0.7389 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:31<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.858 0.591 0.712 0.421\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 34/200 18G 1.092 0.4857 0.7389 126 640: 100% ━━━━━━━━━━━━ 1188/1188 1.2it/s 15:58<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.6sss\n",
" all 52168 159183 0.859 0.591 0.712 0.422\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 35/200 18G 1.087 0.484 0.7386 102 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:35<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:370.6sss\n",
" all 52168 159183 0.86 0.591 0.713 0.423\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 36/200 18G 1.086 0.4836 0.7385 117 640: 100% ━━━━━━━━━━━━ 1188/1188 1.6it/s 12:42<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.6sss\n",
" all 52168 159183 0.859 0.592 0.714 0.423\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 37/200 18G 1.084 0.4811 0.7389 97 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:46<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:430.6sss\n",
" all 52168 159183 0.86 0.592 0.715 0.424\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 38/200 18G 1.079 0.4805 0.7381 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:34<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:310.6sss\n",
" all 52168 159183 0.86 0.593 0.716 0.425\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 39/200 18.4G 1.078 0.479 0.7387 110 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:02<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.5sss\n",
" all 52168 159183 0.86 0.593 0.716 0.426\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 40/200 18G 1.077 0.4789 0.739 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:05<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:290.6sss\n",
" all 52168 159183 0.86 0.593 0.716 0.426\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 41/200 18G 1.075 0.4763 0.7384 107 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:10<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.6sss\n",
" all 52168 159183 0.86 0.593 0.716 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 42/200 18G 1.074 0.4767 0.7384 98 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:02<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.6sss\n",
" all 52168 159183 0.861 0.593 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 43/200 18G 1.072 0.4752 0.7387 90 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:24<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:380.6sss\n",
" all 52168 159183 0.862 0.593 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 44/200 18G 1.073 0.4763 0.7384 102 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:18<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:380.6sss\n",
" all 52168 159183 0.863 0.593 0.717 0.43\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 45/200 18G 1.069 0.4748 0.7379 118 640: 100% ━━━━━━━━━━━━ 1188/1188 1.2it/s 16:39<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.7sss\n",
" all 52168 159183 0.863 0.593 0.718 0.431\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 46/200 18G 1.066 0.4747 0.7382 91 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:58<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:290.6sss\n",
" all 52168 159183 0.863 0.594 0.718 0.431\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 47/200 18G 1.067 0.4751 0.738 95 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:33<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:400.8sss\n",
" all 52168 159183 0.862 0.593 0.718 0.431\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 48/200 18G 1.065 0.4724 0.738 130 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:33<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:310.5sss\n",
" all 52168 159183 0.862 0.593 0.718 0.432\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 49/200 18G 1.065 0.4726 0.7379 126 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:53<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:400.6sss\n",
" all 52168 159183 0.861 0.593 0.717 0.43\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 50/200 18G 1.062 0.4712 0.7381 113 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:60<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.6sss\n",
" all 52168 159183 0.862 0.593 0.718 0.431\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 51/200 18G 1.06 0.4701 0.7381 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:09<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:370.5sss\n",
" all 52168 159183 0.862 0.593 0.717 0.431\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 52/200 18G 1.057 0.4692 0.7378 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:09<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.6sss\n",
" all 52168 159183 0.862 0.593 0.717 0.43\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 53/200 18G 1.059 0.4697 0.7379 116 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:18<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.6sss\n",
" all 52168 159183 0.862 0.592 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 54/200 18G 1.056 0.4684 0.7376 113 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:22<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.6sss\n",
" all 52168 159183 0.861 0.593 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 55/200 18G 1.054 0.4668 0.7376 108 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:37<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.8sss\n",
" all 52168 159183 0.862 0.592 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 56/200 18G 1.052 0.466 0.7376 127 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:25<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.6sss\n",
" all 52168 159183 0.862 0.592 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 57/200 18G 1.052 0.4661 0.7377 105 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:07<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.8it/s 1:460.6sss\n",
" all 52168 159183 0.862 0.592 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 58/200 18G 1.051 0.466 0.7374 112 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:27<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.7sss\n",
" all 52168 159183 0.863 0.592 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 59/200 18G 1.05 0.4645 0.7374 92 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:48<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.864 0.591 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 60/200 18G 1.049 0.4646 0.7376 116 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:09<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.6sss\n",
" all 52168 159183 0.863 0.591 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 61/200 18G 1.047 0.4628 0.738 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:25<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.6sss\n",
" all 52168 159183 0.863 0.591 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 62/200 18G 1.046 0.4625 0.7376 101 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:42<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:350.6sss\n",
" all 52168 159183 0.862 0.591 0.717 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 63/200 18G 1.045 0.462 0.7374 126 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:60<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.5sss\n",
" all 52168 159183 0.862 0.591 0.717 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 64/200 18G 1.047 0.4633 0.7374 110 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:19<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.4sss\n",
" all 52168 159183 0.861 0.591 0.718 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 65/200 18G 1.046 0.4622 0.7374 126 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:07<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.5sss\n",
" all 52168 159183 0.86 0.592 0.717 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 66/200 18G 1.044 0.4623 0.737 103 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:59<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:330.6sss\n",
" all 52168 159183 0.86 0.591 0.717 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 67/200 18G 1.042 0.4612 0.7373 113 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:20<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:370.5sss\n",
" all 52168 159183 0.86 0.591 0.717 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 68/200 18G 1.04 0.4606 0.7373 111 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:09<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.7sss\n",
" all 52168 159183 0.859 0.592 0.717 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 69/200 18G 1.04 0.4602 0.7374 107 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:13<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:410.7sss\n",
" all 52168 159183 0.859 0.592 0.718 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 70/200 18G 1.036 0.4588 0.7374 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:04<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:340.7sss\n",
" all 52168 159183 0.859 0.592 0.718 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 71/200 18G 1.036 0.4582 0.7371 93 640: 100% ━━━━━━━━━━━━ 1188/1188 1.5it/s 13:38<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:400.6sss\n",
" all 52168 159183 0.859 0.592 0.717 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 72/200 18G 1.038 0.4582 0.7371 119 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:37<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.5sss\n",
" all 52168 159183 0.859 0.592 0.718 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 73/200 18G 1.034 0.4574 0.7373 93 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:48<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.6sss\n",
" all 52168 159183 0.86 0.592 0.718 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 74/200 18G 1.036 0.4579 0.7369 103 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:12<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:350.6sss\n",
" all 52168 159183 0.86 0.592 0.719 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 75/200 18G 1.034 0.4565 0.7368 112 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:55<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:420.6sss\n",
" all 52168 159183 0.861 0.593 0.719 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 76/200 18G 1.034 0.4559 0.7372 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:49<1.0s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:400.6sss\n",
" all 52168 159183 0.862 0.593 0.719 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 77/200 18G 1.034 0.4562 0.7368 117 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:55<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 1.9it/s 1:420.6sss\n",
" all 52168 159183 0.862 0.593 0.719 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 78/200 18G 1.032 0.4559 0.7368 122 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:59<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:310.6sss\n",
" all 52168 159183 0.861 0.593 0.719 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 79/200 18G 1.03 0.4553 0.7373 112 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:49<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:310.6sss\n",
" all 52168 159183 0.862 0.593 0.719 0.427\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 80/200 18G 1.03 0.4559 0.7374 131 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 14:44<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:290.5sss\n",
" all 52168 159183 0.862 0.593 0.72 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 81/200 18G 1.028 0.4541 0.7364 105 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:33<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.7sss\n",
" all 52168 159183 0.862 0.593 0.719 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 82/200 18G 1.028 0.4541 0.7371 110 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:25<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.2it/s 1:300.6sss\n",
" all 52168 159183 0.862 0.593 0.72 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 83/200 18G 1.027 0.4526 0.7366 98 640: 100% ━━━━━━━━━━━━ 1188/1188 1.3it/s 15:05<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:390.6sss\n",
" all 52168 159183 0.862 0.593 0.72 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 84/200 18G 1.028 0.4533 0.7365 99 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:21<0.3s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.7sss\n",
" all 52168 159183 0.862 0.592 0.72 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 85/200 18G 1.027 0.4521 0.7367 101 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:48<0.9s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.0it/s 1:360.6sss\n",
" all 52168 159183 0.862 0.592 0.72 0.428\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 86/200 18G 1.025 0.4525 0.7364 108 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 14:24<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.6sss\n",
" all 52168 159183 0.862 0.593 0.72 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 87/200 18G 1.023 0.4501 0.7367 91 640: 100% ━━━━━━━━━━━━ 1188/1188 1.4it/s 13:48<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:310.5sss\n",
" all 52168 159183 0.861 0.593 0.72 0.429\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 88/200 18G 1.023 0.451 0.7363 129 640: 100% ━━━━━━━━━━━━ 1188/1188 1.2it/s 16:46<0.8s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.1it/s 1:320.7sss\n",
" all 52168 159183 0.861 0.593 0.721 0.429\n",
"\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 40 epochs. Best results observed at epoch 48, best model saved as best.pt.\n",
"To update EarlyStopping(patience=40) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
"\n",
"88 epochs completed in 23.032 hours.\n",
"Optimizer stripped from /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22/weights/last.pt, 6.2MB\n",
"Optimizer stripped from /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22/weights/best.pt, 6.2MB\n",
"\n",
"Validating /home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22/weights/best.pt...\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 194/194 2.3it/s 1:230.4sss\n",
" all 52168 159183 0.862 0.593 0.718 0.431\n",
"Speed: 0.0ms preprocess, 0.2ms inference, 0.0ms loss, 0.3ms postprocess per image\n",
"Results saved to \u001b[1m/home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22\u001b[0m\n"
]
}
],
"source": [
"# Load a pretrained YOLO11n model\n",
"model = YOLO(\"yolov8n.pt\")\n",
"\n",
"train_results = model.train(\n",
" data=\"/home/cuuva/experiment/custom_LP_detect/custom_LP.yaml\",\n",
" epochs=200,\n",
" imgsz=640,\n",
" batch= -1,\n",
" device=\"cuda\",\n",
" optimizer = 'AdamW',\n",
" lr0 = 0.001,\n",
" patience = 40,\n",
" project = 'lp_detect',\n",
" name = 'epo_200_frac_0_2',\n",
" fraction = 0.2\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "88d6a47e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"YOLOv8n summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/custom_LP_detect/license_plate_detector.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 5, 8400) (6.0 MB)\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 0.3s, saved as '/home/cuuva/experiment/custom_LP_detect/license_plate_detector.onnx' (11.7 MB)\n",
"\n",
"Export complete (0.4s)\n",
"Results saved to \u001b[1m/home/cuuva/experiment/custom_LP_detect\u001b[0m\n",
"Predict: yolo predict task=detect model=/home/cuuva/experiment/custom_LP_detect/license_plate_detector.onnx imgsz=640 \n",
"Validate: yolo val task=detect model=/home/cuuva/experiment/custom_LP_detect/license_plate_detector.onnx imgsz=640 data=config.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"data": {
"text/plain": [
"'/home/cuuva/experiment/custom_LP_detect/license_plate_detector.onnx'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# model = YOLO(\"/home/cuuva/experiment/custom_LP_detect/lp_detect/epo_200_frac_0_22/weights/best_lp_detect.pt\")\n",
"model = YOLO(\"/home/cuuva/experiment/custom_LP_detect/license_plate_detector.pt\")\n",
"model.export(format=\"onnx\", imgsz=640, device=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0084e5f9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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fashion_yolo/aihub_dataset_eda.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "fc523237",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"================= CLASS STATISTICS =================\n",
"\n",
"📁 Training Dataset\n",
" [0] BLOUSE 👉 9014 개\n",
" [1] COAT 👉 9949 개\n",
" [2] DRESS 👉 10173 개\n",
" [3] JACKET 👉 10466 개\n",
" [4] JUMPER 👉 21893 개\n",
" [5] PANTS 👉 69937 개\n",
" [6] SHIRTS 👉 13438 개\n",
" [7] SKIRT 👉 18156 개\n",
" [8] TSHIRTS 👉 45025 개\n",
"\n",
"\n",
"📁 Validation Dataset\n",
" [0] BLOUSE 👉 1127 개\n",
" [1] COAT 👉 1242 개\n",
" [2] DRESS 👉 1270 개\n",
" [3] JACKET 👉 1309 개\n",
" [4] JUMPER 👉 2737 개\n",
" [5] PANTS 👉 8741 개\n",
" [6] SHIRTS 👉 1681 개\n",
" [7] SKIRT 👉 2269 개\n",
" [8] TSHIRTS 👉 5624 개\n",
"\n",
"\n",
"=============== TOTAL SUMMARY ===============\n",
"\n",
" [0] BLOUSE 👉 10141 total\n",
" [1] COAT 👉 11191 total\n",
" [2] DRESS 👉 11443 total\n",
" [3] JACKET 👉 11775 total\n",
" [4] JUMPER 👉 24630 total\n",
" [5] PANTS 👉 78678 total\n",
" [6] SHIRTS 👉 15119 total\n",
" [7] SKIRT 👉 20425 total\n",
" [8] TSHIRTS 👉 50649 total\n"
]
}
],
"source": [
"import os\n",
"from collections import defaultdict\n",
"\n",
"# ---- CLASS MAP (사용자가 정의한 클래스) ----\n",
"CLASS_MAP = {\n",
" \"0\": \"BLOUSE\",\n",
" \"1\": \"COAT\",\n",
" \"2\": \"DRESS\",\n",
" \"3\": \"JACKET\",\n",
" \"4\": \"JUMPER\",\n",
" \"5\": \"PANTS\",\n",
" \"6\": \"SHIRTS\",\n",
" \"7\": \"SKIRT\",\n",
" \"8\": \"TSHIRTS\"\n",
"}\n",
"\n",
"# ---- 데이터 경로 ----\n",
"DATASETS = {\n",
" \"Training\": \"/home/cuuva/aihub_car/clothes_dataset/Training/labels_txt\",\n",
" \"Validation\": \"/home/cuuva/aihub_car/clothes_dataset/Validation/labels_txt\"\n",
"}\n",
"\n",
"\n",
"def count_classes(path):\n",
" class_count = defaultdict(int)\n",
"\n",
" for root, _, files in os.walk(path):\n",
" for file in files:\n",
" if file.endswith(\".txt\"):\n",
" file_path = os.path.join(root, file)\n",
"\n",
" with open(file_path, \"r\") as f:\n",
" lines = f.readlines()\n",
"\n",
" for line in lines:\n",
" if line.strip():\n",
" class_id = line.split()[0]\n",
" class_count[class_id] += 1\n",
"\n",
" # 정렬\n",
" return dict(sorted(class_count.items(), key=lambda x: int(x[0])))\n",
"\n",
"\n",
"# ---- 실행 ----\n",
"results = {}\n",
"for name, path in DATASETS.items():\n",
" results[name] = count_classes(path)\n",
"\n",
"print(\"\\n================= CLASS STATISTICS =================\\n\")\n",
"\n",
"# ---- 상세 출력 ----\n",
"for dataset_name, counts in results.items():\n",
" print(f\"📁 {dataset_name} Dataset\")\n",
" for cls_id, count in counts.items():\n",
" cls_name = CLASS_MAP.get(cls_id, \"UNKNOWN\")\n",
" print(f\" [{cls_id}] {cls_name:<10} 👉 {count} 개\")\n",
" print(\"\\n\")\n",
"\n",
"\n",
"# ---- Summary 합산 ----\n",
"print(\"=============== TOTAL SUMMARY ===============\\n\")\n",
"total = defaultdict(int)\n",
"\n",
"for r in results.values():\n",
" for cls_id, cnt in r.items():\n",
" total[cls_id] += cnt\n",
"\n",
"for cls_id, cnt in sorted(total.items(), key=lambda x: int(x[0])):\n",
" print(f\" [{cls_id}] {CLASS_MAP[cls_id]:<10} 👉 {cnt} total\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62ada642",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

8225
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106
fashion_yolo/aihub_exp/yolov8s_fashion/args.yaml
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task: detect
mode: train
model: yolov8s.pt
data: /home/cuuva/experiment/fashion_yolo/aihub_fashion.yaml
epochs: 200
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: aihub_exp
name: yolov8s_fashion
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion

27
fashion_yolo/aihub_exp/yolov8s_fashion/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,537.591,0.31592,0.73548,0.96999,0.78711,0.7828,0.83314,0.80005,0.2334,0.57004,0.86224,0.0670252,0.000333079,0.000333079
2,1047.8,0.31123,0.57773,0.96044,0.73462,0.78939,0.8564,0.84804,0.17432,0.50819,0.82295,0.0340219,0.000663114,0.000663114
3,1562.15,0.29818,0.53937,0.95306,0.84157,0.85019,0.91244,0.90544,0.15707,0.36254,0.81149,0.00101525,0.000989848,0.000989848
4,2077.15,0.28065,0.49792,0.94603,0.88001,0.87659,0.93355,0.93232,0.11848,0.2798,0.79165,0.00098515,0.00098515,0.00098515
5,2588.86,0.26853,0.46207,0.94036,0.88174,0.89786,0.94481,0.94383,0.10573,0.24894,0.78506,0.0009802,0.0009802,0.0009802
6,3099.81,0.25891,0.44161,0.93634,0.9048,0.89586,0.9502,0.94945,0.09195,0.22762,0.78043,0.00097525,0.00097525,0.00097525
7,3607.6,0.2526,0.42709,0.93365,0.89822,0.91219,0.95607,0.95559,0.08434,0.20666,0.77541,0.0009703,0.0009703,0.0009703
8,4116.48,0.24902,0.41159,0.93273,0.91249,0.90943,0.95886,0.95852,0.08056,0.19808,0.77446,0.00096535,0.00096535,0.00096535
9,4631.09,0.24722,0.40261,0.93189,0.90378,0.92017,0.96013,0.95987,0.07751,0.19465,0.77274,0.0009604,0.0009604,0.0009604
10,5142.15,0.24136,0.39352,0.92883,0.91669,0.91271,0.96146,0.96127,0.07393,0.18613,0.77182,0.00095545,0.00095545,0.00095545
11,5655.13,0.23686,0.38078,0.92699,0.9137,0.9152,0.96275,0.96259,0.07066,0.1812,0.77011,0.0009505,0.0009505,0.0009505
12,6166.99,0.23468,0.37665,0.9259,0.91422,0.91415,0.96362,0.96342,0.06915,0.17854,0.76943,0.00094555,0.00094555,0.00094555
13,6680.34,0.23247,0.36991,0.92494,0.90222,0.9269,0.96386,0.96368,0.0682,0.17739,0.76895,0.0009406,0.0009406,0.0009406
14,7194.69,0.23053,0.36348,0.92468,0.91431,0.91755,0.96394,0.96378,0.06729,0.17727,0.76854,0.00093565,0.00093565,0.00093565
15,7707.51,0.22804,0.35867,0.92329,0.91653,0.91783,0.96429,0.96412,0.06656,0.17531,0.76825,0.0009307,0.0009307,0.0009307
16,8219.29,0.22732,0.35486,0.92281,0.91488,0.91982,0.96437,0.9642,0.06573,0.17464,0.76796,0.00092575,0.00092575,0.00092575
17,8732.79,0.22742,0.35071,0.92322,0.918,0.91724,0.96453,0.96435,0.06527,0.17411,0.76783,0.0009208,0.0009208,0.0009208
18,9247.94,0.22677,0.34743,0.92351,0.91517,0.91813,0.96462,0.96443,0.06464,0.17382,0.76765,0.00091585,0.00091585,0.00091585
19,9762.34,0.22415,0.34395,0.92166,0.9126,0.91959,0.96468,0.9645,0.06444,0.17379,0.76763,0.0009109,0.0009109,0.0009109
20,10279.6,0.22341,0.33957,0.9218,0.90493,0.92716,0.96487,0.96468,0.06417,0.1738,0.7676,0.00090595,0.00090595,0.00090595
21,10795.6,0.22162,0.33756,0.92031,0.90863,0.92226,0.96492,0.96477,0.06413,0.17355,0.76753,0.000901,0.000901,0.000901
22,11311.9,0.22077,0.33169,0.92081,0.90921,0.92403,0.96489,0.96473,0.06397,0.1732,0.7675,0.00089605,0.00089605,0.00089605
23,11828.2,0.22102,0.33069,0.92076,0.90695,0.92488,0.96507,0.96488,0.06376,0.17277,0.76745,0.0008911,0.0008911,0.0008911
24,12343.3,inf,0.32755,0.92019,0.90925,0.92283,0.96508,0.96493,0.06385,0.17265,0.76749,0.00088615,0.00088615,0.00088615
25,12858.3,0.21934,0.32372,0.92008,0.9166,0.91806,0.96515,0.96495,0.06401,0.17222,0.76752,0.0008812,0.0008812,0.0008812
26,13372.4,0.21782,0.32029,0.91883,0.91643,0.91857,0.96547,0.96526,0.06379,0.17132,0.7674,0.00087625,0.00087625,0.00087625
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 537.591 0.31592 0.73548 0.96999 0.78711 0.7828 0.83314 0.80005 0.2334 0.57004 0.86224 0.0670252 0.000333079 0.000333079
3 2 1047.8 0.31123 0.57773 0.96044 0.73462 0.78939 0.8564 0.84804 0.17432 0.50819 0.82295 0.0340219 0.000663114 0.000663114
4 3 1562.15 0.29818 0.53937 0.95306 0.84157 0.85019 0.91244 0.90544 0.15707 0.36254 0.81149 0.00101525 0.000989848 0.000989848
5 4 2077.15 0.28065 0.49792 0.94603 0.88001 0.87659 0.93355 0.93232 0.11848 0.2798 0.79165 0.00098515 0.00098515 0.00098515
6 5 2588.86 0.26853 0.46207 0.94036 0.88174 0.89786 0.94481 0.94383 0.10573 0.24894 0.78506 0.0009802 0.0009802 0.0009802
7 6 3099.81 0.25891 0.44161 0.93634 0.9048 0.89586 0.9502 0.94945 0.09195 0.22762 0.78043 0.00097525 0.00097525 0.00097525
8 7 3607.6 0.2526 0.42709 0.93365 0.89822 0.91219 0.95607 0.95559 0.08434 0.20666 0.77541 0.0009703 0.0009703 0.0009703
9 8 4116.48 0.24902 0.41159 0.93273 0.91249 0.90943 0.95886 0.95852 0.08056 0.19808 0.77446 0.00096535 0.00096535 0.00096535
10 9 4631.09 0.24722 0.40261 0.93189 0.90378 0.92017 0.96013 0.95987 0.07751 0.19465 0.77274 0.0009604 0.0009604 0.0009604
11 10 5142.15 0.24136 0.39352 0.92883 0.91669 0.91271 0.96146 0.96127 0.07393 0.18613 0.77182 0.00095545 0.00095545 0.00095545
12 11 5655.13 0.23686 0.38078 0.92699 0.9137 0.9152 0.96275 0.96259 0.07066 0.1812 0.77011 0.0009505 0.0009505 0.0009505
13 12 6166.99 0.23468 0.37665 0.9259 0.91422 0.91415 0.96362 0.96342 0.06915 0.17854 0.76943 0.00094555 0.00094555 0.00094555
14 13 6680.34 0.23247 0.36991 0.92494 0.90222 0.9269 0.96386 0.96368 0.0682 0.17739 0.76895 0.0009406 0.0009406 0.0009406
15 14 7194.69 0.23053 0.36348 0.92468 0.91431 0.91755 0.96394 0.96378 0.06729 0.17727 0.76854 0.00093565 0.00093565 0.00093565
16 15 7707.51 0.22804 0.35867 0.92329 0.91653 0.91783 0.96429 0.96412 0.06656 0.17531 0.76825 0.0009307 0.0009307 0.0009307
17 16 8219.29 0.22732 0.35486 0.92281 0.91488 0.91982 0.96437 0.9642 0.06573 0.17464 0.76796 0.00092575 0.00092575 0.00092575
18 17 8732.79 0.22742 0.35071 0.92322 0.918 0.91724 0.96453 0.96435 0.06527 0.17411 0.76783 0.0009208 0.0009208 0.0009208
19 18 9247.94 0.22677 0.34743 0.92351 0.91517 0.91813 0.96462 0.96443 0.06464 0.17382 0.76765 0.00091585 0.00091585 0.00091585
20 19 9762.34 0.22415 0.34395 0.92166 0.9126 0.91959 0.96468 0.9645 0.06444 0.17379 0.76763 0.0009109 0.0009109 0.0009109
21 20 10279.6 0.22341 0.33957 0.9218 0.90493 0.92716 0.96487 0.96468 0.06417 0.1738 0.7676 0.00090595 0.00090595 0.00090595
22 21 10795.6 0.22162 0.33756 0.92031 0.90863 0.92226 0.96492 0.96477 0.06413 0.17355 0.76753 0.000901 0.000901 0.000901
23 22 11311.9 0.22077 0.33169 0.92081 0.90921 0.92403 0.96489 0.96473 0.06397 0.1732 0.7675 0.00089605 0.00089605 0.00089605
24 23 11828.2 0.22102 0.33069 0.92076 0.90695 0.92488 0.96507 0.96488 0.06376 0.17277 0.76745 0.0008911 0.0008911 0.0008911
25 24 12343.3 inf 0.32755 0.92019 0.90925 0.92283 0.96508 0.96493 0.06385 0.17265 0.76749 0.00088615 0.00088615 0.00088615
26 25 12858.3 0.21934 0.32372 0.92008 0.9166 0.91806 0.96515 0.96495 0.06401 0.17222 0.76752 0.0008812 0.0008812 0.0008812
27 26 13372.4 0.21782 0.32029 0.91883 0.91643 0.91857 0.96547 0.96526 0.06379 0.17132 0.7674 0.00087625 0.00087625 0.00087625

7
fashion_yolo/aihub_fashion.yaml
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train: /home/cuuva/aihub_car/clothes_dataset/Training/images/
val: /home/cuuva/aihub_car/clothes_dataset/Validation/images/
# test: /home/cuuva/experiment/datasets/VisDrone/images/test
# nc: 7
nc: 9
names: ['BLOUSE','COAT', 'DRESS', 'JACKET', 'JUMPER', 'PANTS', 'SHIRTS', 'SKIRT', 'TSHIRTS']

439
fashion_yolo/fashion_all.ipynb
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@ -0,0 +1,439 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ed856410",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📂 폴더 분석: /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train\n",
"📄 파일 개수: 45623\n",
"🧮 클래스별 개수:\n",
" class 0 (shirt, blouse): 6161\n",
" class 1 (top, t-shirt, sweatshirt): 16548\n",
" class 2 (sweater): 1494\n",
" class 3 (cardigan): 1107\n",
" class 4 (jacket): 7833\n",
" class 5 (vest): 719\n",
" class 6 (pants): 12414\n",
" class 7 (shorts): 2756\n",
" class 8 (skirt): 5046\n",
" class 9 (coat): 3124\n",
" class 10 (dress): 18739\n",
" class 11 (jumpsuit): 922\n",
" class 12 (cape): 152\n",
" class 13 (glasses): 4855\n",
" class 14 (hat): 2518\n",
" class 15 (hair accessory): 3470\n",
" class 16 (tie): 1457\n",
" class 17 (glove): 1385\n",
" class 18 (watch): 3389\n",
" class 19 (belt): 6851\n",
" class 20 (leg warmer): 112\n",
" class 21 (tights, stockings): 4326\n",
" class 22 (sock): 2582\n",
" class 23 (shoe): 46374\n",
" class 24 (bag, wallet): 7217\n",
" class 25 (scarf): 1374\n",
" class 26 (umbrella): 135\n",
" class 27 (hood): 1226\n",
" class 28 (collar): 10159\n",
" class 29 (lapel): 5972\n",
" class 30 (epaulette): 874\n",
" class 31 (sleeve): 59448\n",
" class 32 (pocket): 27179\n",
" class 33 (neckline): 34258\n",
" class 34 (buckle): 3300\n",
" class 35 (zipper): 7991\n",
" class 36 (applique): 3529\n",
" class 37 (bead): 5084\n",
" class 38 (bow): 528\n",
" class 39 (flower): 1367\n",
" class 40 (fringe): 588\n",
" class 41 (ribbon): 274\n",
" class 42 (rivet): 4893\n",
" class 43 (ruffle): 2407\n",
" class 44 (sequin): 929\n",
" class 45 (tassel): 335\n",
"\n",
"📂 폴더 분석: /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/val\n",
"📄 파일 개수: 1158\n",
"🧮 클래스별 개수:\n",
" class 0 (shirt, blouse): 102\n",
" class 1 (top, t-shirt, sweatshirt): 477\n",
" class 2 (sweater): 21\n",
" class 3 (cardigan): 12\n",
" class 4 (jacket): 183\n",
" class 5 (vest): 22\n",
" class 6 (pants): 314\n",
" class 7 (shorts): 106\n",
" class 8 (skirt): 162\n",
" class 9 (coat): 104\n",
" class 10 (dress): 508\n",
" class 11 (jumpsuit): 21\n",
" class 12 (cape): 5\n",
" class 13 (glasses): 130\n",
" class 14 (hat): 74\n",
" class 15 (hair accessory): 109\n",
" class 16 (tie): 3\n",
" class 17 (glove): 31\n",
" class 18 (watch): 84\n",
" class 19 (belt): 164\n",
" class 20 (leg warmer): 14\n",
" class 21 (tights, stockings): 122\n",
" class 22 (sock): 87\n",
" class 23 (shoe): 1566\n",
" class 24 (bag, wallet): 214\n",
" class 25 (scarf): 48\n",
" class 26 (umbrella): 5\n",
" class 27 (hood): 32\n",
" class 28 (collar): 218\n",
" class 29 (lapel): 135\n",
" class 30 (epaulette): 14\n",
" class 31 (sleeve): 1442\n",
" class 32 (pocket): 541\n",
" class 33 (neckline): 929\n",
" class 34 (buckle): 67\n",
" class 35 (zipper): 194\n",
" class 36 (applique): 61\n",
" class 37 (bead): 107\n",
" class 38 (bow): 6\n",
" class 39 (flower): 37\n",
" class 40 (fringe): 30\n",
" class 41 (ribbon): 9\n",
" class 42 (rivet): 143\n",
" class 43 (ruffle): 76\n",
" class 44 (sequin): 13\n",
" class 45 (tassel): 39\n",
"\n",
"=====================================\n",
"📊 전체(train + val) 클래스별 개수\n",
"=====================================\n",
" class 0 (shirt, blouse): 6263\n",
" class 1 (top, t-shirt, sweatshirt): 17025\n",
" class 2 (sweater): 1515\n",
" class 3 (cardigan): 1119\n",
" class 4 (jacket): 8016\n",
" class 5 (vest): 741\n",
" class 6 (pants): 12728\n",
" class 7 (shorts): 2862\n",
" class 8 (skirt): 5208\n",
" class 9 (coat): 3228\n",
" class 10 (dress): 19247\n",
" class 11 (jumpsuit): 943\n",
" class 12 (cape): 157\n",
" class 13 (glasses): 4985\n",
" class 14 (hat): 2592\n",
" class 15 (hair accessory): 3579\n",
" class 16 (tie): 1460\n",
" class 17 (glove): 1416\n",
" class 18 (watch): 3473\n",
" class 19 (belt): 7015\n",
" class 20 (leg warmer): 126\n",
" class 21 (tights, stockings): 4448\n",
" class 22 (sock): 2669\n",
" class 23 (shoe): 47940\n",
" class 24 (bag, wallet): 7431\n",
" class 25 (scarf): 1422\n",
" class 26 (umbrella): 140\n",
" class 27 (hood): 1258\n",
" class 28 (collar): 10377\n",
" class 29 (lapel): 6107\n",
" class 30 (epaulette): 888\n",
" class 31 (sleeve): 60890\n",
" class 32 (pocket): 27720\n",
" class 33 (neckline): 35187\n",
" class 34 (buckle): 3367\n",
" class 35 (zipper): 8185\n",
" class 36 (applique): 3590\n",
" class 37 (bead): 5191\n",
" class 38 (bow): 534\n",
" class 39 (flower): 1404\n",
" class 40 (fringe): 618\n",
" class 41 (ribbon): 283\n",
" class 42 (rivet): 5036\n",
" class 43 (ruffle): 2483\n",
" class 44 (sequin): 942\n",
" class 45 (tassel): 374\n"
]
}
],
"source": [
"import os\n",
"from collections import defaultdict\n",
"import yaml\n",
"\n",
"# ---------------------------------------\n",
"# 1) YAML 파일에서 클래스 이름 불러오기\n",
"# ---------------------------------------\n",
"yaml_path = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml\"\n",
"\n",
"with open(yaml_path, \"r\") as f:\n",
" data = yaml.safe_load(f)\n",
"\n",
"names = data[\"names\"]\n",
"# keys가 문자열일 수도 있음 → 정수 key로 맞춰줌\n",
"class_names = {int(k): v for k, v in names.items()}\n",
"\n",
"# ---------------------------------------\n",
"# 2) Label 파일 읽어서 클래스별 개수 계산\n",
"# ---------------------------------------\n",
"label_root = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/labels\"\n",
"folders = [\"train\", \"val\"]\n",
"\n",
"total_counts = defaultdict(int)\n",
"\n",
"for folder in folders:\n",
" folder_path = os.path.join(label_root, folder)\n",
" class_counts = defaultdict(int)\n",
"\n",
" txt_files = [f for f in os.listdir(folder_path) if f.endswith(\".txt\")]\n",
"\n",
" print(f\"\\n📂 폴더 분석: {folder_path}\")\n",
" print(f\"📄 파일 개수: {len(txt_files)}\")\n",
"\n",
" for txt in txt_files:\n",
" with open(os.path.join(folder_path, txt), \"r\") as f:\n",
" lines = f.readlines()\n",
"\n",
" for line in lines:\n",
" class_id = int(line.split()[0])\n",
" class_counts[class_id] += 1\n",
" total_counts[class_id] += 1\n",
"\n",
" # --- 폴더별 결과 출력 ---\n",
" print(\"🧮 클래스별 개수:\")\n",
" for cid in sorted(class_counts.keys()):\n",
" print(f\" class {cid:2d} ({class_names.get(cid, 'Unknown')}): {class_counts[cid]}\")\n",
"\n",
"# ---------------------------------------\n",
"# 3) 전체 합산 결과 출력\n",
"# ---------------------------------------\n",
"print(\"\\n=====================================\")\n",
"print(\"📊 전체(train + val) 클래스별 개수\")\n",
"print(\"=====================================\")\n",
"for cid in sorted(total_counts.keys()):\n",
" print(f\" class {cid:2d} ({class_names.get(cid, 'Unknown')}): {total_counts[cid]}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d94803f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New https://pypi.org/project/ultralytics/8.3.237 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=500, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=yolov8m_fashion_all, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=50, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=fashionpedia_exp, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=46\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2] \n",
" 1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2] \n",
" 2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True] \n",
" 3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] \n",
" 4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True] \n",
" 5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2] \n",
" 6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True] \n",
" 7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2] \n",
" 8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True] \n",
" 9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2] \n",
" 16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2] \n",
" 19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] \n",
" 22 [15, 18, 21] 1 3802330 ultralytics.nn.modules.head.Detect [46, [192, 384, 576]] \n",
"Model summary: 169 layers, 25,882,954 parameters, 25,882,938 gradients, 79.2 GFLOPs\n",
"\n",
"Transferred 469/475 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 3322.6±3977.1 MB/s, size: 88.2 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train... 45623 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 45623/45623 7.9Kit/s 5.8s0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/fashionpedia_yolo/images/train/19725.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train.cache\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.36G total, 0.25G reserved, 0.24G allocated, 30.87G free\n",
" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cuuva/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 25882954 79.21 2.898 28.43 155.4 (1, 3, 640, 640) list\n",
" 25882954 158.4 4.754 9.878 36.17 (2, 3, 640, 640) list\n",
" 25882954 316.8 5.887 13.6 50 (4, 3, 640, 640) list\n",
" 25882954 633.7 8.972 26.37 74.09 (8, 3, 640, 640) list\n",
" 25882954 1267 12.149 27.28 121.9 (16, 3, 640, 640) list\n",
" 25882954 2535 20.816 55.09 243.1 (32, 3, 640, 640) list\n",
" 25882954 5069 20.615 117.7 484.3 (64, 3, 640, 640) list\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 28 for CUDA:0 19.32G/31.36G (62%) ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 5333.7±2302.8 MB/s, size: 66.6 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train.cache... 45623 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 45623/45623 90.6Mit/s 0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/fashionpedia_yolo/images/train/19725.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 3235.1±1428.7 MB/s, size: 71.1 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/val... 1158 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1158/1158 6.4Kit/s 0.2s4s\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/fashionpedia_yolo/labels/val.cache\n",
"Plotting labels to /home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0004375), 83 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1m/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all\u001b[0m\n",
"Starting training for 500 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 1/500 12.3G 1.118 1.526 1.247 379 640: 76% ━━━━━━━━━─── 1232/1630 8.0it/s 5:18<49.7s\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m model \u001b[38;5;241m=\u001b[39m YOLO(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myolov8m.pt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# 2. 학습 실행\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# 위에서 생성된 yaml 파일 경로를 넣어줍니다.\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m train_results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m640\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAdamW\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfashionpedia_exp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myolov8m_fashion_all\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/model.py:777\u001b[0m, in \u001b[0;36mModel.train\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mget_model(weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mckpt \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, cfg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39myaml)\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 778\u001b[0m \u001b[38;5;66;03m# Update model and cfg after training\u001b[39;00m\n\u001b[1;32m 779\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m}:\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/trainer.py:238\u001b[0m, in \u001b[0;36mBaseTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 235\u001b[0m ddp_cleanup(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mstr\u001b[39m(file))\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/trainer.py:422\u001b[0m, in \u001b[0;36mBaseTrainer._do_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 420\u001b[0m loss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_items \u001b[38;5;241m=\u001b[39m unwrap_model(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\u001b[38;5;241m.\u001b[39mloss(batch, preds)\n\u001b[1;32m 421\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 422\u001b[0m loss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_items \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/nn/modules/module.py:1775\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/nn/modules/module.py:1786\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1781\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1782\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1783\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1784\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1785\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1786\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1788\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1789\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/nn/tasks.py:136\u001b[0m, in \u001b[0;36mBaseModel.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Perform forward pass of the model for either training or inference.\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \n\u001b[1;32m 125\u001b[0m \u001b[38;5;124;03mIf x is a dict, calculates and returns the loss for training. Otherwise, returns predictions for inference.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;124;03m (torch.Tensor): Loss if x is a dict (training), or network predictions (inference).\u001b[39;00m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m): \u001b[38;5;66;03m# for cases of training and validating while training.\u001b[39;00m\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/nn/tasks.py:328\u001b[0m, in \u001b[0;36mBaseModel.loss\u001b[0;34m(self, batch, preds)\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 327\u001b[0m preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforward(batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m--> 328\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/utils/loss.py:270\u001b[0m, in \u001b[0;36mv8DetectionLoss.__call__\u001b[0;34m(self, preds, batch)\u001b[0m\n\u001b[1;32m 266\u001b[0m pred_bboxes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbbox_decode(anchor_points, pred_distri) \u001b[38;5;66;03m# xyxy, (b, h*w, 4)\u001b[39;00m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# dfl_conf = pred_distri.view(batch_size, -1, 4, self.reg_max).detach().softmax(-1)\u001b[39;00m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;66;03m# dfl_conf = (dfl_conf.amax(-1).mean(-1) + dfl_conf.amax(-1).amin(-1)) / 2\u001b[39;00m\n\u001b[0;32m--> 270\u001b[0m _, target_bboxes, target_scores, fg_mask, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massigner\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pred_scores.detach().sigmoid() * 0.8 + dfl_conf.unsqueeze(-1) * 0.2,\u001b[39;49;00m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43mpred_scores\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msigmoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred_bboxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstride_tensor\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgt_bboxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43manchor_points\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstride_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mgt_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[43mgt_bboxes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask_gt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 278\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 280\u001b[0m target_scores_sum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(target_scores\u001b[38;5;241m.\u001b[39msum(), \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 282\u001b[0m \u001b[38;5;66;03m# Cls loss\u001b[39;00m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;66;03m# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/nn/modules/module.py:1775\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/nn/modules/module.py:1786\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1781\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1782\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1783\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1784\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1785\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1786\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1788\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1789\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/utils/_contextlib.py:120\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 120\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/utils/tal.py:79\u001b[0m, in \u001b[0;36mTaskAlignedAssigner.forward\u001b[0;34m(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m 71\u001b[0m torch\u001b[38;5;241m.\u001b[39mfull_like(pd_scores[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_classes),\n\u001b[1;32m 72\u001b[0m torch\u001b[38;5;241m.\u001b[39mzeros_like(pd_bboxes),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 75\u001b[0m torch\u001b[38;5;241m.\u001b[39mzeros_like(pd_scores[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m]),\n\u001b[1;32m 76\u001b[0m )\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpd_scores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpd_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manc_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask_gt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mOutOfMemoryError:\n\u001b[1;32m 81\u001b[0m \u001b[38;5;66;03m# Move tensors to CPU, compute, then move back to original device\u001b[39;00m\n\u001b[1;32m 82\u001b[0m LOGGER\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCUDA OutOfMemoryError in TaskAlignedAssigner, using CPU\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/utils/tal.py:105\u001b[0m, in \u001b[0;36mTaskAlignedAssigner._forward\u001b[0;34m(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):\n\u001b[1;32m 88\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the task-aligned assignment.\u001b[39;00m\n\u001b[1;32m 89\u001b[0m \n\u001b[1;32m 90\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;124;03m target_gt_idx (torch.Tensor): Target ground truth indices with shape (bs, num_total_anchors).\u001b[39;00m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m mask_pos, align_metric, overlaps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_pos_mask\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mpd_scores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpd_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manc_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask_gt\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 109\u001b[0m target_gt_idx, fg_mask, mask_pos \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mselect_highest_overlaps(mask_pos, overlaps, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_max_boxes)\n\u001b[1;32m 111\u001b[0m \u001b[38;5;66;03m# Assigned target\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/utils/tal.py:141\u001b[0m, in \u001b[0;36mTaskAlignedAssigner.get_pos_mask\u001b[0;34m(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt)\u001b[0m\n\u001b[1;32m 139\u001b[0m mask_in_gts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mselect_candidates_in_gts(anc_points, gt_bboxes)\n\u001b[1;32m 140\u001b[0m \u001b[38;5;66;03m# Get anchor_align metric, (b, max_num_obj, h*w)\u001b[39;00m\n\u001b[0;32m--> 141\u001b[0m align_metric, overlaps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_box_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpd_scores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpd_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask_in_gts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmask_gt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;66;03m# Get topk_metric mask, (b, max_num_obj, h*w)\u001b[39;00m\n\u001b[1;32m 143\u001b[0m mask_topk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mselect_topk_candidates(align_metric, topk_mask\u001b[38;5;241m=\u001b[39mmask_gt\u001b[38;5;241m.\u001b[39mexpand(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopk)\u001b[38;5;241m.\u001b[39mbool())\n",
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/utils/tal.py:175\u001b[0m, in \u001b[0;36mTaskAlignedAssigner.get_box_metrics\u001b[0;34m(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt)\u001b[0m\n\u001b[1;32m 172\u001b[0m bbox_scores[mask_gt] \u001b[38;5;241m=\u001b[39m pd_scores[ind[\u001b[38;5;241m0\u001b[39m], :, ind[\u001b[38;5;241m1\u001b[39m]][mask_gt] \u001b[38;5;66;03m# b, max_num_obj, h*w\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# (b, max_num_obj, 1, 4), (b, 1, h*w, 4)\u001b[39;00m\n\u001b[0;32m--> 175\u001b[0m pd_boxes \u001b[38;5;241m=\u001b[39m \u001b[43mpd_bboxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mexpand(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_max_boxes, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)[mask_gt]\n\u001b[1;32m 176\u001b[0m gt_boxes \u001b[38;5;241m=\u001b[39m gt_bboxes\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mexpand(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, na, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)[mask_gt]\n\u001b[1;32m 177\u001b[0m overlaps[mask_gt] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miou_calculation(gt_boxes, pd_boxes)\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"from ultralytics import YOLO\n",
"\n",
"# 1. 모델 로드 (YOLOv8m 사용)\n",
"model = YOLO('yolov8m.pt')\n",
"\n",
"# 2. 학습 실행\n",
"# 위에서 생성된 yaml 파일 경로를 넣어줍니다.\n",
"train_results = model.train(\n",
" data=\"/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml\", \n",
" epochs=500, \n",
" imgsz=640,\n",
" batch=-1, \n",
" device=\"cuda\",\n",
" optimizer='AdamW',\n",
" lr0=0.001,\n",
" patience=50,\n",
" project='fashionpedia_exp',\n",
" name='yolov8m_fashion_all',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9de5e25b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n",
"Model summary (fused): 92 layers, 25,866,394 parameters, 0 gradients, 78.8 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 50, 8400) (49.7 MB)\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 0.9s, saved as '/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.onnx' (98.9 MB)\n",
"\n",
"Export complete (1.4s)\n",
"Results saved to \u001b[1m/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights\u001b[0m\n",
"Predict: yolo predict task=detect model=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.onnx imgsz=640 \n",
"Validate: yolo val task=detect model=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.onnx imgsz=640 data=/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"data": {
"text/plain": [
"'/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.onnx'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from ultralytics import YOLO\n",
"model = YOLO(\"/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/weights/best_fashion_detect_all.pt\")\n",
"model.export(format=\"onnx\", imgsz=640, device=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a77ad845",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

106
fashion_yolo/fashionpedia_exp/yolov8m_fashion+face/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_custom.yaml
epochs: 500
time: null
patience: 100
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: fashionpedia_exp
name: yolov8m_fashion+face
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face

149
fashion_yolo/fashionpedia_exp/yolov8m_fashion+face/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,467.057,0.81693,1.12746,1.11612,0.54533,0.39001,0.3928,0.29481,0.83096,0.9068,1.04598,0.0670304,0.000333027,0.000333027
2,935.528,0.80969,1.01999,1.11239,0.58249,0.4265,0.45098,0.34294,0.79094,0.83315,1.02486,0.034029,0.000665041,0.000665041
3,1403.24,0.77545,0.95443,1.0934,0.60362,0.48918,0.53296,0.42692,0.66897,0.69128,0.96869,0.0010264,0.000995735,0.000995735
4,1867.28,0.69526,0.84232,1.04912,0.55647,0.57369,0.5656,0.45762,0.63462,0.631,0.95357,0.00099406,0.00099406,0.00099406
5,2331.34,0.66974,0.8034,1.03568,0.67283,0.517,0.58531,0.48487,0.60306,0.59998,0.94113,0.00099208,0.00099208,0.00099208
6,2795.27,0.65377,0.77574,1.02698,0.61599,0.58064,0.60414,0.49684,0.58512,0.58784,0.93346,0.0009901,0.0009901,0.0009901
7,3258.75,0.63937,0.75073,1.01797,0.62045,0.58785,0.62562,0.51724,0.5769,0.56478,0.93021,0.00098812,0.00098812,0.00098812
8,3722.58,0.62693,0.73014,1.01106,0.69556,0.61687,0.66601,0.55158,0.56688,0.53685,0.92476,0.00098614,0.00098614,0.00098614
9,4186.46,0.61949,0.7154,1.00696,0.67596,0.62899,0.66082,0.55676,0.5523,0.53072,0.91756,0.00098416,0.00098416,0.00098416
10,4650.19,0.60957,0.70193,1.00275,0.73885,0.62388,0.67504,0.56706,0.54429,0.51469,0.91708,0.00098218,0.00098218,0.00098218
11,5114.28,0.60425,0.68863,0.99893,0.69136,0.64269,0.67954,0.5778,0.53208,0.50031,0.90859,0.0009802,0.0009802,0.0009802
12,5578.44,0.59478,0.67847,0.99458,0.7075,0.63317,0.69369,0.59201,0.52279,0.49295,0.904,0.00097822,0.00097822,0.00097822
13,6042.37,0.5887,0.66591,0.9908,0.76371,0.62884,0.70385,0.60097,0.51638,0.48391,0.90036,0.00097624,0.00097624,0.00097624
14,6512.06,0.58261,0.65644,0.98777,0.74316,0.652,0.71859,0.61435,0.5078,0.47993,0.89855,0.00097426,0.00097426,0.00097426
15,6976.05,0.57974,0.64695,0.9851,0.75534,0.65491,0.72066,0.61845,0.50818,0.47139,0.89856,0.00097228,0.00097228,0.00097228
16,7440.32,0.57205,0.6371,0.98256,0.7202,0.68237,0.7228,0.62345,0.50565,0.46069,0.89822,0.0009703,0.0009703,0.0009703
17,7904.66,0.56831,0.62784,0.98011,0.74443,0.67453,0.72164,0.62184,0.49883,0.45869,0.89593,0.00096832,0.00096832,0.00096832
18,8368.77,0.564,0.62095,0.97782,0.78102,0.66905,0.73814,0.6364,0.49399,0.45245,0.89525,0.00096634,0.00096634,0.00096634
19,8833.21,0.56125,0.61312,0.97591,0.78344,0.67802,0.74498,0.64284,0.49224,0.45233,0.89184,0.00096436,0.00096436,0.00096436
20,9291.53,0.55746,0.60413,0.97227,0.74325,0.69505,0.74347,0.6409,0.48917,0.44875,0.89043,0.00096238,0.00096238,0.00096238
21,9749.03,0.55175,0.59783,0.97195,0.71419,0.7172,0.7511,0.65253,0.48452,0.44122,0.88902,0.0009604,0.0009604,0.0009604
22,10206.1,0.54774,0.59105,0.97042,0.75634,0.70744,0.76003,0.66179,0.48349,0.43958,0.88969,0.00095842,0.00095842,0.00095842
23,10663.1,0.54698,0.58527,0.96958,0.79148,0.70269,0.76288,0.66043,0.47955,0.43799,0.88831,0.00095644,0.00095644,0.00095644
24,11120.1,0.54339,0.57915,0.96632,0.7589,0.72312,0.75601,0.65612,0.47758,0.43662,0.88685,0.00095446,0.00095446,0.00095446
25,11576.9,0.53908,0.57337,0.966,0.75346,0.72104,0.75521,0.65778,0.47665,0.43568,0.88706,0.00095248,0.00095248,0.00095248
26,12034.2,0.53659,0.56695,0.96264,0.76518,0.71979,0.75664,0.66037,0.47398,0.43574,0.88628,0.0009505,0.0009505,0.0009505
27,12491.6,0.53434,0.5621,0.9611,0.75036,0.72955,0.75745,0.66184,0.47184,0.4337,0.88512,0.00094852,0.00094852,0.00094852
28,12948.7,0.53181,0.55533,0.95866,0.76131,0.71555,0.76207,0.66557,0.47057,0.43263,0.88423,0.00094654,0.00094654,0.00094654
29,13406,0.52823,0.55253,0.95889,0.7727,0.71127,0.76412,0.66749,0.46989,0.43108,0.8842,0.00094456,0.00094456,0.00094456
30,13863.4,0.52584,0.54711,0.95825,0.78324,0.70842,0.76374,0.66892,0.46828,0.43001,0.88362,0.00094258,0.00094258,0.00094258
31,14321.1,0.52382,0.54327,0.95609,0.79574,0.70657,0.76396,0.66882,0.46715,0.42887,0.88314,0.0009406,0.0009406,0.0009406
32,14778.2,0.52337,0.53802,0.95528,0.7811,0.71463,0.76726,0.67271,0.46727,0.42801,0.88386,0.00093862,0.00093862,0.00093862
33,15235.6,0.51967,0.5331,0.95356,0.78128,0.71613,0.76771,0.67353,0.46689,0.42677,0.88364,0.00093664,0.00093664,0.00093664
34,15692.4,0.52036,0.53114,0.95386,0.77695,0.71925,0.77107,0.6759,0.46628,0.42561,0.88331,0.00093466,0.00093466,0.00093466
35,16149.8,0.51783,0.52731,0.95243,0.7758,0.72485,0.77273,0.6774,0.46583,0.42431,0.8835,0.00093268,0.00093268,0.00093268
36,16607.1,0.51416,0.52285,0.95134,0.7807,0.72425,0.77242,0.67756,0.46569,0.42426,0.88394,0.0009307,0.0009307,0.0009307
37,17064.7,0.51309,0.51764,0.94941,0.78396,0.71824,0.77182,0.67722,0.46545,0.42395,0.88402,0.00092872,0.00092872,0.00092872
38,17521.7,0.51026,0.51577,0.95043,0.78847,0.71526,0.77277,0.6786,0.46515,0.42321,0.88431,0.00092674,0.00092674,0.00092674
39,17978.9,0.50843,0.50893,0.94436,0.78724,0.71607,0.77334,0.67763,0.46416,0.42294,0.88322,0.00092476,0.00092476,0.00092476
40,18436,0.50889,0.50743,0.94803,0.78949,0.71617,0.77456,0.67961,0.46341,0.42322,0.88274,0.00092278,0.00092278,0.00092278
41,18893.8,0.50464,0.5022,0.94493,0.78714,0.72178,0.77634,0.68105,0.46338,0.42328,0.88275,0.0009208,0.0009208,0.0009208
42,19351.1,0.50424,0.49693,0.94267,0.78618,0.7212,0.77657,0.68135,0.46306,0.4243,0.88246,0.00091882,0.00091882,0.00091882
43,19808.8,0.50157,0.49782,0.9415,0.78257,0.72396,0.77632,0.68339,0.46261,0.42337,0.88195,0.00091684,0.00091684,0.00091684
44,20265.3,0.50046,0.49344,0.94201,0.78197,0.72435,0.77725,0.68406,0.46146,0.42295,0.88097,0.00091486,0.00091486,0.00091486
45,20722.9,0.50013,0.4901,0.94178,0.77366,0.72358,0.77725,0.68436,0.46148,0.42276,0.88064,0.00091288,0.00091288,0.00091288
46,21179.9,0.49718,0.48691,0.93874,0.77525,0.72767,0.77668,0.68472,0.46082,0.42207,0.8803,0.0009109,0.0009109,0.0009109
47,21637.4,0.49619,0.48468,0.94083,0.77476,0.72626,0.77704,0.68558,0.46006,0.42076,0.87981,0.00090892,0.00090892,0.00090892
48,22094.2,0.496,0.48359,0.94045,0.77279,0.72727,0.77778,0.68623,0.45911,0.41981,0.87924,0.00090694,0.00090694,0.00090694
49,22551.1,0.49226,0.47864,0.93811,0.77877,0.7243,0.77754,0.68554,0.45885,0.4194,0.87887,0.00090496,0.00090496,0.00090496
50,23008,0.49142,0.47693,0.93952,0.78685,0.72566,0.77839,0.68674,0.45826,0.41808,0.87876,0.00090298,0.00090298,0.00090298
51,23465.2,0.49063,0.47228,0.93746,0.78727,0.71737,0.77756,0.68611,0.45794,0.41835,0.8789,0.000901,0.000901,0.000901
52,23922.4,0.49277,0.47346,0.93698,0.79896,0.71359,0.77682,0.68592,0.45764,0.41908,0.87888,0.00089902,0.00089902,0.00089902
53,24379.3,0.48874,0.4681,0.93659,0.80174,0.7089,0.77739,0.68646,0.45716,0.41974,0.87853,0.00089704,0.00089704,0.00089704
54,24836.1,0.48684,0.46737,0.93502,0.80014,0.71472,0.7776,0.68744,0.45694,0.41981,0.87846,0.00089506,0.00089506,0.00089506
55,25293,0.48609,0.46297,0.93441,0.80951,0.70669,0.7783,0.68803,0.45686,0.41992,0.87849,0.00089308,0.00089308,0.00089308
56,25749.6,0.48778,0.46382,0.9338,0.82277,0.70775,0.77801,0.68791,0.45693,0.42021,0.87861,0.0008911,0.0008911,0.0008911
57,26207.1,0.48345,0.4615,0.9339,0.81973,0.70701,0.77692,0.68636,0.45705,0.421,0.8786,0.00088912,0.00088912,0.00088912
58,26663.7,0.48171,0.45549,0.93135,0.78953,0.70906,0.7758,0.68529,0.45698,0.42079,0.87847,0.00088714,0.00088714,0.00088714
59,27120.8,0.4829,0.45529,0.93213,0.79176,0.70614,0.7739,0.68357,0.4568,0.42125,0.87816,0.00088516,0.00088516,0.00088516
60,27577.2,0.48051,0.45312,0.93211,0.80325,0.70479,0.77414,0.68308,0.45658,0.42082,0.87806,0.00088318,0.00088318,0.00088318
61,28034,0.47899,0.45065,0.93165,0.79931,0.70471,0.77423,0.68257,0.45691,0.4208,0.87855,0.0008812,0.0008812,0.0008812
62,28491,0.47958,0.44895,0.93011,0.79137,0.70373,0.7737,0.68239,0.45634,0.42075,0.87815,0.00087922,0.00087922,0.00087922
63,28948.1,0.4767,0.44567,0.93035,0.79866,0.69783,0.77472,0.68291,0.45611,0.42045,0.87829,0.00087724,0.00087724,0.00087724
64,29405.3,0.47688,0.44559,0.9299,0.77824,0.70628,0.77419,0.68323,0.45611,0.42006,0.87866,0.00087526,0.00087526,0.00087526
65,29862.4,0.47644,0.4422,0.92813,0.76677,0.7267,0.77378,0.68248,0.45633,0.42018,0.8792,0.00087328,0.00087328,0.00087328
66,30319.4,0.47647,0.44285,0.92968,0.7673,0.72557,0.77459,0.68248,0.45623,0.42038,0.87927,0.0008713,0.0008713,0.0008713
67,30776.4,0.47252,0.43681,0.92775,0.76907,0.7251,0.77551,0.68324,0.45608,0.42079,0.87911,0.00086932,0.00086932,0.00086932
68,31233.3,0.4711,0.43378,0.92607,0.76942,0.72093,0.77494,0.6832,0.45554,0.42074,0.87866,0.00086734,0.00086734,0.00086734
69,31690.7,0.47249,0.43583,0.92663,0.7645,0.73173,0.77535,0.68276,0.45504,0.42129,0.87827,0.00086536,0.00086536,0.00086536
70,32147.6,0.46949,0.43015,0.92474,0.75754,0.73054,0.77827,0.68592,0.45442,0.42064,0.8779,0.00086338,0.00086338,0.00086338
71,32604.7,0.46907,0.42979,0.92388,0.74692,0.7441,0.77871,0.68564,0.45433,0.42132,0.87786,0.0008614,0.0008614,0.0008614
72,33061.5,0.46846,0.42863,0.92253,0.73621,0.75454,0.77869,0.68633,0.45423,0.42121,0.87761,0.00085942,0.00085942,0.00085942
73,33518.5,0.46762,0.42615,0.92471,0.73202,0.75631,0.77545,0.68323,0.45405,0.42108,0.87764,0.00085744,0.00085744,0.00085744
74,33975.5,0.46791,0.42613,0.9243,0.73907,0.75753,0.77318,0.68249,0.45351,0.42158,0.87765,0.00085546,0.00085546,0.00085546
75,34432.5,0.46608,0.42433,0.92408,0.73007,0.76396,0.7725,0.68094,0.45398,0.42129,0.8783,0.00085348,0.00085348,0.00085348
76,34889.9,0.46584,0.4229,0.92379,0.73199,0.76402,0.77224,0.68086,0.45426,0.42198,0.87884,0.0008515,0.0008515,0.0008515
77,35347.3,0.46535,0.42133,0.92254,0.81113,0.69475,0.77201,0.68101,0.45363,0.42174,0.87846,0.00084952,0.00084952,0.00084952
78,35804.2,0.46361,0.41818,0.9219,0.79895,0.69762,0.77107,0.68005,0.45401,0.42224,0.87876,0.00084754,0.00084754,0.00084754
79,36261.3,0.46416,0.41925,0.92238,0.76515,0.72391,0.76805,0.6782,0.45346,0.42263,0.87855,0.00084556,0.00084556,0.00084556
80,36718,0.46402,0.41817,0.92145,0.76437,0.72538,0.76851,0.67819,0.4533,0.4224,0.87875,0.00084358,0.00084358,0.00084358
81,37175.1,0.46216,0.41539,0.92017,0.76568,0.72454,0.76822,0.67813,0.45346,0.42295,0.87895,0.0008416,0.0008416,0.0008416
82,37631.6,0.46253,0.41288,0.92032,0.77806,0.71767,0.76966,0.67924,0.45352,0.42376,0.87891,0.00083962,0.00083962,0.00083962
83,38089.1,0.45927,0.41287,0.91951,0.7818,0.7217,0.76831,0.67754,0.4534,0.42399,0.87897,0.00083764,0.00083764,0.00083764
84,38545.8,0.45915,0.41203,0.92189,0.78707,0.71863,0.76771,0.67804,0.45279,0.42425,0.87869,0.00083566,0.00083566,0.00083566
85,39002.6,0.45858,0.40899,0.92037,0.79273,0.72032,0.76862,0.67897,0.45243,0.42485,0.87879,0.00083368,0.00083368,0.00083368
86,39459.3,0.45864,0.40922,0.91975,0.78657,0.72246,0.76958,0.67928,0.45183,0.42487,0.87864,0.0008317,0.0008317,0.0008317
87,39916.5,0.45708,0.40611,0.91709,0.78493,0.72216,0.77005,0.67945,0.45196,0.42528,0.87889,0.00082972,0.00082972,0.00082972
88,40373.2,0.45586,0.40475,0.9177,0.7873,0.72054,0.76952,0.6804,0.45172,0.42557,0.87891,0.00082774,0.00082774,0.00082774
89,40830.3,0.45607,0.40352,0.91685,0.78946,0.72047,0.77068,0.68128,0.45137,0.42593,0.8786,0.00082576,0.00082576,0.00082576
90,41287,0.45555,0.40366,0.91769,0.78649,0.72242,0.77133,0.68226,0.45131,0.42603,0.8788,0.00082378,0.00082378,0.00082378
91,41743.7,0.45334,0.40325,0.91687,0.78225,0.72174,0.77248,0.6835,0.45121,0.42558,0.87871,0.0008218,0.0008218,0.0008218
92,42200.1,0.4523,0.3988,0.91459,0.78005,0.71967,0.77139,0.68284,0.45084,0.42535,0.87843,0.00081982,0.00081982,0.00081982
93,42657.3,0.45389,0.39987,0.91523,0.77945,0.72395,0.76969,0.68103,0.45064,0.42562,0.87839,0.00081784,0.00081784,0.00081784
94,43114.3,0.45189,0.39892,0.91509,0.77935,0.72389,0.76837,0.68026,0.45048,0.42592,0.87853,0.00081586,0.00081586,0.00081586
95,43571,0.45095,0.39702,0.91544,0.78362,0.7192,0.76941,0.68133,0.45038,0.42666,0.87875,0.00081388,0.00081388,0.00081388
96,44027.8,0.45043,0.39434,0.9143,0.78529,0.72434,0.76974,0.68066,0.4502,0.42693,0.87872,0.0008119,0.0008119,0.0008119
97,44485,0.44907,0.39346,0.91497,0.78972,0.72503,0.77126,0.68284,0.45032,0.42606,0.87929,0.00080992,0.00080992,0.00080992
98,44941.6,0.4501,0.39176,0.91463,0.79343,0.7235,0.77175,0.68363,0.4496,0.42576,0.8788,0.00080794,0.00080794,0.00080794
99,45398.3,0.4472,0.39053,0.91264,0.78797,0.7241,0.77215,0.68449,0.44971,0.42459,0.87906,0.00080596,0.00080596,0.00080596
100,45854.9,0.44797,0.39095,0.91435,0.79764,0.71761,0.77162,0.68358,0.44971,0.42364,0.87945,0.00080398,0.00080398,0.00080398
101,46312.2,0.44859,0.38778,0.91432,0.79517,0.7157,0.77186,0.68362,0.44986,0.42318,0.88002,0.000802,0.000802,0.000802
102,46769.3,0.44737,0.38979,0.91256,0.79498,0.71184,0.77005,0.68133,0.44934,0.42342,0.87958,0.00080002,0.00080002,0.00080002
103,47226.3,0.44651,0.39091,0.9139,0.80105,0.70571,0.77037,0.68103,0.4489,0.42247,0.87931,0.00079804,0.00079804,0.00079804
104,47683.2,0.44593,0.38576,0.91242,0.79937,0.70537,0.76903,0.68048,0.4488,0.4227,0.87918,0.00079606,0.00079606,0.00079606
105,48140,0.44384,0.38419,0.91217,0.79167,0.70701,0.76807,0.67955,0.44872,0.42306,0.8795,0.00079408,0.00079408,0.00079408
106,48596.6,0.44397,0.38284,0.91164,0.79326,0.70713,0.76659,0.67955,0.44855,0.42309,0.87927,0.0007921,0.0007921,0.0007921
107,49053.9,0.44312,0.38283,0.91175,0.76835,0.72471,0.76487,0.67789,0.44841,0.4238,0.87918,0.00079012,0.00079012,0.00079012
108,49510.8,0.44293,0.38274,0.91101,0.80969,0.6922,0.76355,0.67723,0.44833,0.42412,0.87931,0.00078814,0.00078814,0.00078814
109,49967.4,inf,0.38063,0.90988,0.81147,0.69346,0.76203,0.67542,0.44789,0.42471,0.87914,0.00078616,0.00078616,0.00078616
110,50424.1,0.44198,0.38007,0.90952,0.81468,0.69532,0.76076,0.67456,0.44781,0.42463,0.87923,0.00078418,0.00078418,0.00078418
111,50880.9,0.44111,0.37779,0.90967,0.81108,0.69234,0.75889,0.67367,0.44788,0.42456,0.8794,0.0007822,0.0007822,0.0007822
112,51337.8,0.44019,0.37729,0.90865,0.8026,0.69656,0.75878,0.67267,0.44748,0.4252,0.87944,0.00078022,0.00078022,0.00078022
113,51794.9,0.44043,0.37656,0.90976,0.78372,0.7083,0.76074,0.67461,0.44746,0.4256,0.87976,0.00077824,0.00077824,0.00077824
114,52251.7,0.43925,0.37616,0.90879,0.77615,0.70974,0.75946,0.67339,0.44721,0.42609,0.87991,0.00077626,0.00077626,0.00077626
115,52708.4,0.43886,0.37515,0.90909,0.78646,0.70411,0.75797,0.67266,0.44755,0.42701,0.88027,0.00077428,0.00077428,0.00077428
116,53165,0.43715,0.37307,0.90821,0.78496,0.70646,0.7562,0.67198,0.44714,0.42773,0.88016,0.0007723,0.0007723,0.0007723
117,53622,0.43647,0.37361,0.90851,0.78533,0.70883,0.75648,0.67042,0.44714,0.42884,0.88031,0.00077032,0.00077032,0.00077032
118,54079.1,0.43673,0.37222,0.90782,0.78882,0.70904,0.75651,0.67085,0.44741,0.42964,0.88068,0.00076834,0.00076834,0.00076834
119,54536,0.43743,0.37078,0.90754,0.78309,0.71043,0.7564,0.67002,0.44741,0.42995,0.88094,0.00076636,0.00076636,0.00076636
120,54992.3,0.43669,0.37062,0.90803,0.7753,0.71363,0.75385,0.66899,0.44744,0.42999,0.88135,0.00076438,0.00076438,0.00076438
121,55449,0.4334,0.36937,0.9063,0.75993,0.71958,0.75377,0.66952,0.44729,0.43001,0.88123,0.0007624,0.0007624,0.0007624
122,55905.5,0.43429,0.36782,0.90624,0.78227,0.71082,0.75477,0.67058,0.44722,0.43052,0.88107,0.00076042,0.00076042,0.00076042
123,56361.9,0.43469,0.36758,0.90525,0.78583,0.71081,0.75801,0.67238,0.44749,0.43031,0.88172,0.00075844,0.00075844,0.00075844
124,56818.7,0.43462,0.36938,0.90565,0.77139,0.72738,0.75849,0.67203,0.4475,0.4298,0.88158,0.00075646,0.00075646,0.00075646
125,57275.2,0.43339,0.36394,0.90478,0.76959,0.72729,0.75694,0.67113,0.44741,0.43019,0.88165,0.00075448,0.00075448,0.00075448
126,57732,0.43394,0.36448,0.90473,0.76693,0.72795,0.75688,0.67172,0.44695,0.42989,0.88112,0.0007525,0.0007525,0.0007525
127,58189.2,0.43253,0.36469,0.90486,0.76826,0.72626,0.75508,0.67017,0.44693,0.42962,0.88127,0.00075052,0.00075052,0.00075052
128,58646.1,0.43239,0.36374,0.90486,0.76666,0.72359,0.75467,0.66914,0.44678,0.4302,0.88114,0.00074854,0.00074854,0.00074854
129,59103.1,0.43185,0.36314,0.90531,0.76291,0.72795,0.75327,0.66787,0.44668,0.4322,0.88144,0.00074656,0.00074656,0.00074656
130,59559.7,0.43216,0.36144,0.905,0.75751,0.72855,0.75448,0.66962,0.44644,0.43195,0.88184,0.00074458,0.00074458,0.00074458
131,60016.9,0.43098,0.36052,0.90641,0.76098,0.73172,0.75348,0.66778,0.44635,0.43226,0.88217,0.0007426,0.0007426,0.0007426
132,60473.4,0.43107,0.36141,0.90544,0.7615,0.73175,0.75313,0.6673,0.44648,0.43262,0.88259,0.00074062,0.00074062,0.00074062
133,60929.8,0.42975,0.3596,0.90564,0.77032,0.72718,0.75407,0.66704,0.44623,0.43226,0.88259,0.00073864,0.00073864,0.00073864
134,61386.8,0.42853,0.35903,0.90297,0.7678,0.71816,0.75102,0.66464,0.44645,0.43118,0.88286,0.00073666,0.00073666,0.00073666
135,61844,0.42759,0.35641,0.90307,0.77209,0.72126,0.75152,0.665,0.44621,0.4314,0.88258,0.00073468,0.00073468,0.00073468
136,62300.6,0.42683,0.35587,0.90236,0.77269,0.7198,0.7507,0.66488,0.4463,0.43073,0.88273,0.0007327,0.0007327,0.0007327
137,62757.6,0.42793,0.35752,0.90495,0.75066,0.73559,0.75081,0.66467,0.44612,0.43051,0.88277,0.00073072,0.00073072,0.00073072
138,63213.9,0.42776,0.35659,0.90466,0.7662,0.71974,0.75074,0.66386,0.44603,0.42897,0.88269,0.00072874,0.00072874,0.00072874
139,63670.7,0.42712,0.35382,0.90171,0.75651,0.7333,0.75219,0.66594,0.44645,0.43025,0.88289,0.00072676,0.00072676,0.00072676
140,64127.5,0.42492,0.35263,0.90012,0.76756,0.73341,0.75203,0.66597,0.4459,0.42972,0.88227,0.00072478,0.00072478,0.00072478
141,64583.9,0.42503,0.35342,0.90313,0.76668,0.73627,0.7521,0.66657,0.44533,0.42909,0.88172,0.0007228,0.0007228,0.0007228
142,65044.8,0.42461,0.34968,0.90277,0.76568,0.73795,0.75339,0.66688,0.44561,0.42896,0.8817,0.00072082,0.00072082,0.00072082
143,65508.3,0.42521,0.35163,0.90093,0.75986,0.73199,0.75521,0.6692,0.44553,0.42939,0.88175,0.00071884,0.00071884,0.00071884
144,65971.9,0.42471,0.35297,0.89987,0.75769,0.73147,0.75438,0.66957,0.44525,0.42913,0.88107,0.00071686,0.00071686,0.00071686
145,66435.6,0.42376,0.34929,0.90032,0.74797,0.74298,0.75231,0.66701,0.44528,0.42973,0.88125,0.00071488,0.00071488,0.00071488
146,66899.6,0.4228,0.34943,0.90095,0.74808,0.74264,0.7509,0.66617,0.44551,0.42927,0.88129,0.0007129,0.0007129,0.0007129
147,67363.4,0.42311,0.35028,0.9019,0.75525,0.73759,0.75126,0.66572,0.44516,0.42961,0.88102,0.00071092,0.00071092,0.00071092
148,67827,0.42366,0.34835,0.90192,0.76592,0.73358,0.75174,0.66591,0.44523,0.42977,0.88092,0.00070894,0.00070894,0.00070894
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 467.057 0.81693 1.12746 1.11612 0.54533 0.39001 0.3928 0.29481 0.83096 0.9068 1.04598 0.0670304 0.000333027 0.000333027
3 2 935.528 0.80969 1.01999 1.11239 0.58249 0.4265 0.45098 0.34294 0.79094 0.83315 1.02486 0.034029 0.000665041 0.000665041
4 3 1403.24 0.77545 0.95443 1.0934 0.60362 0.48918 0.53296 0.42692 0.66897 0.69128 0.96869 0.0010264 0.000995735 0.000995735
5 4 1867.28 0.69526 0.84232 1.04912 0.55647 0.57369 0.5656 0.45762 0.63462 0.631 0.95357 0.00099406 0.00099406 0.00099406
6 5 2331.34 0.66974 0.8034 1.03568 0.67283 0.517 0.58531 0.48487 0.60306 0.59998 0.94113 0.00099208 0.00099208 0.00099208
7 6 2795.27 0.65377 0.77574 1.02698 0.61599 0.58064 0.60414 0.49684 0.58512 0.58784 0.93346 0.0009901 0.0009901 0.0009901
8 7 3258.75 0.63937 0.75073 1.01797 0.62045 0.58785 0.62562 0.51724 0.5769 0.56478 0.93021 0.00098812 0.00098812 0.00098812
9 8 3722.58 0.62693 0.73014 1.01106 0.69556 0.61687 0.66601 0.55158 0.56688 0.53685 0.92476 0.00098614 0.00098614 0.00098614
10 9 4186.46 0.61949 0.7154 1.00696 0.67596 0.62899 0.66082 0.55676 0.5523 0.53072 0.91756 0.00098416 0.00098416 0.00098416
11 10 4650.19 0.60957 0.70193 1.00275 0.73885 0.62388 0.67504 0.56706 0.54429 0.51469 0.91708 0.00098218 0.00098218 0.00098218
12 11 5114.28 0.60425 0.68863 0.99893 0.69136 0.64269 0.67954 0.5778 0.53208 0.50031 0.90859 0.0009802 0.0009802 0.0009802
13 12 5578.44 0.59478 0.67847 0.99458 0.7075 0.63317 0.69369 0.59201 0.52279 0.49295 0.904 0.00097822 0.00097822 0.00097822
14 13 6042.37 0.5887 0.66591 0.9908 0.76371 0.62884 0.70385 0.60097 0.51638 0.48391 0.90036 0.00097624 0.00097624 0.00097624
15 14 6512.06 0.58261 0.65644 0.98777 0.74316 0.652 0.71859 0.61435 0.5078 0.47993 0.89855 0.00097426 0.00097426 0.00097426
16 15 6976.05 0.57974 0.64695 0.9851 0.75534 0.65491 0.72066 0.61845 0.50818 0.47139 0.89856 0.00097228 0.00097228 0.00097228
17 16 7440.32 0.57205 0.6371 0.98256 0.7202 0.68237 0.7228 0.62345 0.50565 0.46069 0.89822 0.0009703 0.0009703 0.0009703
18 17 7904.66 0.56831 0.62784 0.98011 0.74443 0.67453 0.72164 0.62184 0.49883 0.45869 0.89593 0.00096832 0.00096832 0.00096832
19 18 8368.77 0.564 0.62095 0.97782 0.78102 0.66905 0.73814 0.6364 0.49399 0.45245 0.89525 0.00096634 0.00096634 0.00096634
20 19 8833.21 0.56125 0.61312 0.97591 0.78344 0.67802 0.74498 0.64284 0.49224 0.45233 0.89184 0.00096436 0.00096436 0.00096436
21 20 9291.53 0.55746 0.60413 0.97227 0.74325 0.69505 0.74347 0.6409 0.48917 0.44875 0.89043 0.00096238 0.00096238 0.00096238
22 21 9749.03 0.55175 0.59783 0.97195 0.71419 0.7172 0.7511 0.65253 0.48452 0.44122 0.88902 0.0009604 0.0009604 0.0009604
23 22 10206.1 0.54774 0.59105 0.97042 0.75634 0.70744 0.76003 0.66179 0.48349 0.43958 0.88969 0.00095842 0.00095842 0.00095842
24 23 10663.1 0.54698 0.58527 0.96958 0.79148 0.70269 0.76288 0.66043 0.47955 0.43799 0.88831 0.00095644 0.00095644 0.00095644
25 24 11120.1 0.54339 0.57915 0.96632 0.7589 0.72312 0.75601 0.65612 0.47758 0.43662 0.88685 0.00095446 0.00095446 0.00095446
26 25 11576.9 0.53908 0.57337 0.966 0.75346 0.72104 0.75521 0.65778 0.47665 0.43568 0.88706 0.00095248 0.00095248 0.00095248
27 26 12034.2 0.53659 0.56695 0.96264 0.76518 0.71979 0.75664 0.66037 0.47398 0.43574 0.88628 0.0009505 0.0009505 0.0009505
28 27 12491.6 0.53434 0.5621 0.9611 0.75036 0.72955 0.75745 0.66184 0.47184 0.4337 0.88512 0.00094852 0.00094852 0.00094852
29 28 12948.7 0.53181 0.55533 0.95866 0.76131 0.71555 0.76207 0.66557 0.47057 0.43263 0.88423 0.00094654 0.00094654 0.00094654
30 29 13406 0.52823 0.55253 0.95889 0.7727 0.71127 0.76412 0.66749 0.46989 0.43108 0.8842 0.00094456 0.00094456 0.00094456
31 30 13863.4 0.52584 0.54711 0.95825 0.78324 0.70842 0.76374 0.66892 0.46828 0.43001 0.88362 0.00094258 0.00094258 0.00094258
32 31 14321.1 0.52382 0.54327 0.95609 0.79574 0.70657 0.76396 0.66882 0.46715 0.42887 0.88314 0.0009406 0.0009406 0.0009406
33 32 14778.2 0.52337 0.53802 0.95528 0.7811 0.71463 0.76726 0.67271 0.46727 0.42801 0.88386 0.00093862 0.00093862 0.00093862
34 33 15235.6 0.51967 0.5331 0.95356 0.78128 0.71613 0.76771 0.67353 0.46689 0.42677 0.88364 0.00093664 0.00093664 0.00093664
35 34 15692.4 0.52036 0.53114 0.95386 0.77695 0.71925 0.77107 0.6759 0.46628 0.42561 0.88331 0.00093466 0.00093466 0.00093466
36 35 16149.8 0.51783 0.52731 0.95243 0.7758 0.72485 0.77273 0.6774 0.46583 0.42431 0.8835 0.00093268 0.00093268 0.00093268
37 36 16607.1 0.51416 0.52285 0.95134 0.7807 0.72425 0.77242 0.67756 0.46569 0.42426 0.88394 0.0009307 0.0009307 0.0009307
38 37 17064.7 0.51309 0.51764 0.94941 0.78396 0.71824 0.77182 0.67722 0.46545 0.42395 0.88402 0.00092872 0.00092872 0.00092872
39 38 17521.7 0.51026 0.51577 0.95043 0.78847 0.71526 0.77277 0.6786 0.46515 0.42321 0.88431 0.00092674 0.00092674 0.00092674
40 39 17978.9 0.50843 0.50893 0.94436 0.78724 0.71607 0.77334 0.67763 0.46416 0.42294 0.88322 0.00092476 0.00092476 0.00092476
41 40 18436 0.50889 0.50743 0.94803 0.78949 0.71617 0.77456 0.67961 0.46341 0.42322 0.88274 0.00092278 0.00092278 0.00092278
42 41 18893.8 0.50464 0.5022 0.94493 0.78714 0.72178 0.77634 0.68105 0.46338 0.42328 0.88275 0.0009208 0.0009208 0.0009208
43 42 19351.1 0.50424 0.49693 0.94267 0.78618 0.7212 0.77657 0.68135 0.46306 0.4243 0.88246 0.00091882 0.00091882 0.00091882
44 43 19808.8 0.50157 0.49782 0.9415 0.78257 0.72396 0.77632 0.68339 0.46261 0.42337 0.88195 0.00091684 0.00091684 0.00091684
45 44 20265.3 0.50046 0.49344 0.94201 0.78197 0.72435 0.77725 0.68406 0.46146 0.42295 0.88097 0.00091486 0.00091486 0.00091486
46 45 20722.9 0.50013 0.4901 0.94178 0.77366 0.72358 0.77725 0.68436 0.46148 0.42276 0.88064 0.00091288 0.00091288 0.00091288
47 46 21179.9 0.49718 0.48691 0.93874 0.77525 0.72767 0.77668 0.68472 0.46082 0.42207 0.8803 0.0009109 0.0009109 0.0009109
48 47 21637.4 0.49619 0.48468 0.94083 0.77476 0.72626 0.77704 0.68558 0.46006 0.42076 0.87981 0.00090892 0.00090892 0.00090892
49 48 22094.2 0.496 0.48359 0.94045 0.77279 0.72727 0.77778 0.68623 0.45911 0.41981 0.87924 0.00090694 0.00090694 0.00090694
50 49 22551.1 0.49226 0.47864 0.93811 0.77877 0.7243 0.77754 0.68554 0.45885 0.4194 0.87887 0.00090496 0.00090496 0.00090496
51 50 23008 0.49142 0.47693 0.93952 0.78685 0.72566 0.77839 0.68674 0.45826 0.41808 0.87876 0.00090298 0.00090298 0.00090298
52 51 23465.2 0.49063 0.47228 0.93746 0.78727 0.71737 0.77756 0.68611 0.45794 0.41835 0.8789 0.000901 0.000901 0.000901
53 52 23922.4 0.49277 0.47346 0.93698 0.79896 0.71359 0.77682 0.68592 0.45764 0.41908 0.87888 0.00089902 0.00089902 0.00089902
54 53 24379.3 0.48874 0.4681 0.93659 0.80174 0.7089 0.77739 0.68646 0.45716 0.41974 0.87853 0.00089704 0.00089704 0.00089704
55 54 24836.1 0.48684 0.46737 0.93502 0.80014 0.71472 0.7776 0.68744 0.45694 0.41981 0.87846 0.00089506 0.00089506 0.00089506
56 55 25293 0.48609 0.46297 0.93441 0.80951 0.70669 0.7783 0.68803 0.45686 0.41992 0.87849 0.00089308 0.00089308 0.00089308
57 56 25749.6 0.48778 0.46382 0.9338 0.82277 0.70775 0.77801 0.68791 0.45693 0.42021 0.87861 0.0008911 0.0008911 0.0008911
58 57 26207.1 0.48345 0.4615 0.9339 0.81973 0.70701 0.77692 0.68636 0.45705 0.421 0.8786 0.00088912 0.00088912 0.00088912
59 58 26663.7 0.48171 0.45549 0.93135 0.78953 0.70906 0.7758 0.68529 0.45698 0.42079 0.87847 0.00088714 0.00088714 0.00088714
60 59 27120.8 0.4829 0.45529 0.93213 0.79176 0.70614 0.7739 0.68357 0.4568 0.42125 0.87816 0.00088516 0.00088516 0.00088516
61 60 27577.2 0.48051 0.45312 0.93211 0.80325 0.70479 0.77414 0.68308 0.45658 0.42082 0.87806 0.00088318 0.00088318 0.00088318
62 61 28034 0.47899 0.45065 0.93165 0.79931 0.70471 0.77423 0.68257 0.45691 0.4208 0.87855 0.0008812 0.0008812 0.0008812
63 62 28491 0.47958 0.44895 0.93011 0.79137 0.70373 0.7737 0.68239 0.45634 0.42075 0.87815 0.00087922 0.00087922 0.00087922
64 63 28948.1 0.4767 0.44567 0.93035 0.79866 0.69783 0.77472 0.68291 0.45611 0.42045 0.87829 0.00087724 0.00087724 0.00087724
65 64 29405.3 0.47688 0.44559 0.9299 0.77824 0.70628 0.77419 0.68323 0.45611 0.42006 0.87866 0.00087526 0.00087526 0.00087526
66 65 29862.4 0.47644 0.4422 0.92813 0.76677 0.7267 0.77378 0.68248 0.45633 0.42018 0.8792 0.00087328 0.00087328 0.00087328
67 66 30319.4 0.47647 0.44285 0.92968 0.7673 0.72557 0.77459 0.68248 0.45623 0.42038 0.87927 0.0008713 0.0008713 0.0008713
68 67 30776.4 0.47252 0.43681 0.92775 0.76907 0.7251 0.77551 0.68324 0.45608 0.42079 0.87911 0.00086932 0.00086932 0.00086932
69 68 31233.3 0.4711 0.43378 0.92607 0.76942 0.72093 0.77494 0.6832 0.45554 0.42074 0.87866 0.00086734 0.00086734 0.00086734
70 69 31690.7 0.47249 0.43583 0.92663 0.7645 0.73173 0.77535 0.68276 0.45504 0.42129 0.87827 0.00086536 0.00086536 0.00086536
71 70 32147.6 0.46949 0.43015 0.92474 0.75754 0.73054 0.77827 0.68592 0.45442 0.42064 0.8779 0.00086338 0.00086338 0.00086338
72 71 32604.7 0.46907 0.42979 0.92388 0.74692 0.7441 0.77871 0.68564 0.45433 0.42132 0.87786 0.0008614 0.0008614 0.0008614
73 72 33061.5 0.46846 0.42863 0.92253 0.73621 0.75454 0.77869 0.68633 0.45423 0.42121 0.87761 0.00085942 0.00085942 0.00085942
74 73 33518.5 0.46762 0.42615 0.92471 0.73202 0.75631 0.77545 0.68323 0.45405 0.42108 0.87764 0.00085744 0.00085744 0.00085744
75 74 33975.5 0.46791 0.42613 0.9243 0.73907 0.75753 0.77318 0.68249 0.45351 0.42158 0.87765 0.00085546 0.00085546 0.00085546
76 75 34432.5 0.46608 0.42433 0.92408 0.73007 0.76396 0.7725 0.68094 0.45398 0.42129 0.8783 0.00085348 0.00085348 0.00085348
77 76 34889.9 0.46584 0.4229 0.92379 0.73199 0.76402 0.77224 0.68086 0.45426 0.42198 0.87884 0.0008515 0.0008515 0.0008515
78 77 35347.3 0.46535 0.42133 0.92254 0.81113 0.69475 0.77201 0.68101 0.45363 0.42174 0.87846 0.00084952 0.00084952 0.00084952
79 78 35804.2 0.46361 0.41818 0.9219 0.79895 0.69762 0.77107 0.68005 0.45401 0.42224 0.87876 0.00084754 0.00084754 0.00084754
80 79 36261.3 0.46416 0.41925 0.92238 0.76515 0.72391 0.76805 0.6782 0.45346 0.42263 0.87855 0.00084556 0.00084556 0.00084556
81 80 36718 0.46402 0.41817 0.92145 0.76437 0.72538 0.76851 0.67819 0.4533 0.4224 0.87875 0.00084358 0.00084358 0.00084358
82 81 37175.1 0.46216 0.41539 0.92017 0.76568 0.72454 0.76822 0.67813 0.45346 0.42295 0.87895 0.0008416 0.0008416 0.0008416
83 82 37631.6 0.46253 0.41288 0.92032 0.77806 0.71767 0.76966 0.67924 0.45352 0.42376 0.87891 0.00083962 0.00083962 0.00083962
84 83 38089.1 0.45927 0.41287 0.91951 0.7818 0.7217 0.76831 0.67754 0.4534 0.42399 0.87897 0.00083764 0.00083764 0.00083764
85 84 38545.8 0.45915 0.41203 0.92189 0.78707 0.71863 0.76771 0.67804 0.45279 0.42425 0.87869 0.00083566 0.00083566 0.00083566
86 85 39002.6 0.45858 0.40899 0.92037 0.79273 0.72032 0.76862 0.67897 0.45243 0.42485 0.87879 0.00083368 0.00083368 0.00083368
87 86 39459.3 0.45864 0.40922 0.91975 0.78657 0.72246 0.76958 0.67928 0.45183 0.42487 0.87864 0.0008317 0.0008317 0.0008317
88 87 39916.5 0.45708 0.40611 0.91709 0.78493 0.72216 0.77005 0.67945 0.45196 0.42528 0.87889 0.00082972 0.00082972 0.00082972
89 88 40373.2 0.45586 0.40475 0.9177 0.7873 0.72054 0.76952 0.6804 0.45172 0.42557 0.87891 0.00082774 0.00082774 0.00082774
90 89 40830.3 0.45607 0.40352 0.91685 0.78946 0.72047 0.77068 0.68128 0.45137 0.42593 0.8786 0.00082576 0.00082576 0.00082576
91 90 41287 0.45555 0.40366 0.91769 0.78649 0.72242 0.77133 0.68226 0.45131 0.42603 0.8788 0.00082378 0.00082378 0.00082378
92 91 41743.7 0.45334 0.40325 0.91687 0.78225 0.72174 0.77248 0.6835 0.45121 0.42558 0.87871 0.0008218 0.0008218 0.0008218
93 92 42200.1 0.4523 0.3988 0.91459 0.78005 0.71967 0.77139 0.68284 0.45084 0.42535 0.87843 0.00081982 0.00081982 0.00081982
94 93 42657.3 0.45389 0.39987 0.91523 0.77945 0.72395 0.76969 0.68103 0.45064 0.42562 0.87839 0.00081784 0.00081784 0.00081784
95 94 43114.3 0.45189 0.39892 0.91509 0.77935 0.72389 0.76837 0.68026 0.45048 0.42592 0.87853 0.00081586 0.00081586 0.00081586
96 95 43571 0.45095 0.39702 0.91544 0.78362 0.7192 0.76941 0.68133 0.45038 0.42666 0.87875 0.00081388 0.00081388 0.00081388
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fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/args.yaml
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task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_custom.yaml
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time: null
patience: 50
batch: -1
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device: '0'
workers: 8
project: fashionpedia_exp
name: yolov8m_fashion+face_nohood
exist_ok: false
pretrained: true
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105
fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/results.csv
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47,19470,0.49727,0.49241,0.94068,0.7627,0.74122,0.79872,0.70583,0.45167,0.41558,0.8735,0.00090892,0.00090892,0.00090892
48,19883.1,0.49796,0.48956,0.94143,0.76406,0.73882,0.79846,0.70615,0.45138,0.41527,0.87353,0.00090694,0.00090694,0.00090694
49,20296.5,0.49546,0.48413,0.94004,0.77804,0.73915,0.79651,0.7049,0.45036,0.41469,0.873,0.00090496,0.00090496,0.00090496
50,20709.9,0.49374,0.48193,0.93856,0.75715,0.74998,0.79761,0.70672,0.44862,0.41322,0.87172,0.00090298,0.00090298,0.00090298
51,21123,0.49173,0.48042,0.93761,0.75312,0.76114,0.80086,0.70989,0.4478,0.41251,0.87141,0.000901,0.000901,0.000901
52,21536.1,0.4925,0.47896,0.93668,0.75894,0.75716,0.79916,0.70979,0.44683,0.41208,0.8711,0.00089902,0.00089902,0.00089902
53,21949.3,0.49059,0.47373,0.93603,0.75033,0.75753,0.79993,0.71,0.44632,0.41236,0.87075,0.00089704,0.00089704,0.00089704
54,22362.7,0.48891,0.47249,0.93646,0.74364,0.76568,0.79959,0.71227,0.44535,0.41234,0.87075,0.00089506,0.00089506,0.00089506
55,22776.2,0.48682,0.46793,0.93362,0.74023,0.76995,0.79986,0.71232,0.44468,0.41223,0.87038,0.00089308,0.00089308,0.00089308
56,23189.5,0.48631,0.46959,0.93358,0.75056,0.76026,0.80061,0.71176,0.44416,0.4118,0.8703,0.0008911,0.0008911,0.0008911
57,23607.7,0.48619,0.46501,0.93519,0.7537,0.76494,0.79862,0.71104,0.44394,0.41235,0.87053,0.00088912,0.00088912,0.00088912
58,24020.7,0.48476,0.46277,0.93324,0.75743,0.76102,0.79932,0.71131,0.44358,0.41228,0.87041,0.00088714,0.00088714,0.00088714
59,24433.8,0.48166,0.46254,0.93205,0.76176,0.75102,0.79827,0.709,0.44337,0.41178,0.87058,0.00088516,0.00088516,0.00088516
60,24847.1,0.48212,0.45845,0.93135,0.76823,0.75006,0.79928,0.71042,0.44323,0.41134,0.87067,0.00088318,0.00088318,0.00088318
61,25260.4,0.47819,0.45481,0.92939,0.78356,0.73803,0.79808,0.7088,0.44319,0.41177,0.87088,0.0008812,0.0008812,0.0008812
62,25676.3,0.48044,0.4541,0.92984,0.79393,0.73299,0.79838,0.70863,0.44272,0.41204,0.8708,0.00087922,0.00087922,0.00087922
63,26089.6,0.47791,0.45112,0.92915,0.78968,0.73772,0.79925,0.71099,0.44254,0.41099,0.87055,0.00087724,0.00087724,0.00087724
64,26502.8,0.47806,0.44986,0.92901,0.80092,0.732,0.80017,0.71352,0.44219,0.41138,0.87009,0.00087526,0.00087526,0.00087526
65,26915.6,0.476,0.4468,0.92896,0.78978,0.73796,0.79886,0.71154,0.44215,0.4119,0.86973,0.00087328,0.00087328,0.00087328
66,27322.5,0.47772,0.44785,0.92943,0.79266,0.73314,0.79866,0.71153,0.44217,0.41167,0.86992,0.0008713,0.0008713,0.0008713
67,27729.6,0.47233,0.44218,0.92638,0.78418,0.73931,0.79885,0.71062,0.44223,0.41196,0.8701,0.00086932,0.00086932,0.00086932
68,28136.5,0.47261,0.44138,0.92722,0.78131,0.7393,0.80012,0.71136,0.44195,0.41121,0.87033,0.00086734,0.00086734,0.00086734
69,28543.3,0.47434,0.44198,0.92755,0.76545,0.75512,0.80294,0.71495,0.4414,0.41223,0.8702,0.00086536,0.00086536,0.00086536
70,28950,0.47358,0.44024,0.92585,0.77268,0.75158,0.80198,0.71476,0.44142,0.41306,0.87043,0.00086338,0.00086338,0.00086338
71,29357.1,0.47182,0.44245,0.927,0.77147,0.75214,0.80304,0.71586,0.44129,0.41262,0.8709,0.0008614,0.0008614,0.0008614
72,29764.1,0.4699,0.43587,0.92728,0.75905,0.75395,0.80313,0.71603,0.44134,0.41219,0.87116,0.00085942,0.00085942,0.00085942
73,30171.5,0.47064,0.43401,0.92513,0.75106,0.75756,0.80196,0.71564,0.44075,0.41377,0.87084,0.00085744,0.00085744,0.00085744
74,30578.8,0.46963,0.43396,0.92446,0.74647,0.76236,0.79796,0.71287,0.44047,0.41486,0.87063,0.00085546,0.00085546,0.00085546
75,30986,0.46753,0.4297,0.92304,0.74813,0.76113,0.79784,0.71295,0.44099,0.41523,0.87126,0.00085348,0.00085348,0.00085348
76,31393.1,0.46816,0.42883,0.92469,0.75173,0.76017,0.79417,0.70999,0.44127,0.41529,0.87185,0.0008515,0.0008515,0.0008515
77,31800,0.46755,0.42557,0.9221,0.75028,0.75999,0.79398,0.70994,0.44089,0.41509,0.87155,0.00084952,0.00084952,0.00084952
78,32207.2,0.46605,0.42535,0.92224,0.74602,0.76574,0.79463,0.71033,0.44037,0.41484,0.8715,0.00084754,0.00084754,0.00084754
79,32614.3,0.46419,0.42386,0.92196,0.74853,0.7638,0.79435,0.71007,0.44025,0.41418,0.87156,0.00084556,0.00084556,0.00084556
80,33021.3,0.46331,0.42135,0.92009,0.74608,0.76806,0.79346,0.71001,0.44013,0.41416,0.87146,0.00084358,0.00084358,0.00084358
81,33428.3,0.46329,0.42098,0.9203,0.73533,0.77353,0.79369,0.70978,0.44014,0.41412,0.87161,0.0008416,0.0008416,0.0008416
82,33838.2,0.46204,0.41945,0.9202,0.72866,0.76873,0.79184,0.70796,0.43985,0.41397,0.87148,0.00083962,0.00083962,0.00083962
83,34245.4,0.46047,0.4179,0.9196,0.73688,0.764,0.79546,0.71093,0.43961,0.41322,0.87154,0.00083764,0.00083764,0.00083764
84,34652.4,0.46059,0.41616,0.91913,0.757,0.7515,0.79431,0.71142,0.43956,0.41412,0.87181,0.00083566,0.00083566,0.00083566
85,35059.2,0.45804,0.41332,0.91884,0.75192,0.75519,0.79216,0.71057,0.43903,0.4147,0.87125,0.00083368,0.00083368,0.00083368
86,35465.6,0.45891,0.4142,0.91914,0.74396,0.75136,0.79133,0.7107,0.43902,0.41553,0.87161,0.0008317,0.0008317,0.0008317
87,35872.3,0.45788,0.41118,0.91874,0.7342,0.7607,0.78855,0.70827,0.43848,0.41534,0.87155,0.00082972,0.00082972,0.00082972
88,36279.4,0.45947,0.41375,0.91884,0.74042,0.75982,0.78883,0.70845,0.43781,0.4151,0.87105,0.00082774,0.00082774,0.00082774
89,36686.3,0.45737,0.40988,0.91824,0.74366,0.75101,0.78688,0.70468,0.43765,0.41532,0.87122,0.00082576,0.00082576,0.00082576
90,37093.4,0.45558,0.40892,0.91977,0.74055,0.74452,0.78835,0.70488,0.43702,0.41471,0.87083,0.00082378,0.00082378,0.00082378
91,37500.1,0.45482,0.40572,0.91678,0.71077,0.7772,0.78449,0.70238,0.43658,0.4165,0.87078,0.0008218,0.0008218,0.0008218
92,37906.9,0.45494,0.40508,0.91603,0.75222,0.74549,0.78572,0.70291,0.43645,0.41604,0.87073,0.00081982,0.00081982,0.00081982
93,38313.7,0.45338,0.40396,0.91692,0.75845,0.73926,0.78497,0.70213,0.43607,0.41614,0.87065,0.00081784,0.00081784,0.00081784
94,38720.6,0.45335,0.40144,0.91629,0.77968,0.72014,0.78322,0.69995,0.43559,0.41663,0.87041,0.00081586,0.00081586,0.00081586
95,39127.2,0.4518,0.40143,0.9156,0.73032,0.7702,0.77981,0.69603,0.43578,0.41742,0.87058,0.00081388,0.00081388,0.00081388
96,39533.9,0.45207,0.4002,0.91584,0.73874,0.76339,0.78094,0.69774,0.43591,0.41834,0.87081,0.0008119,0.0008119,0.0008119
97,39940.6,0.45152,0.39845,0.91479,0.78103,0.7198,0.78089,0.69749,0.43635,0.41902,0.87149,0.00080992,0.00080992,0.00080992
98,40347.3,0.44954,0.39821,0.91471,0.7782,0.72363,0.78284,0.69964,0.43642,0.41986,0.87176,0.00080794,0.00080794,0.00080794
99,40754.4,0.44988,0.39499,0.91411,0.77651,0.72593,0.78467,0.70062,0.43628,0.42137,0.8718,0.00080596,0.00080596,0.00080596
100,41161.2,0.44889,0.3957,0.91274,0.75338,0.74382,0.78477,0.70181,0.43602,0.42178,0.87143,0.00080398,0.00080398,0.00080398
101,41568,0.44779,0.39306,0.91275,0.75099,0.74695,0.7843,0.70103,0.43644,0.42216,0.87127,0.000802,0.000802,0.000802
102,41975.1,0.44856,0.39426,0.91325,0.75858,0.73667,0.7809,0.69774,0.43662,0.42299,0.87155,0.00080002,0.00080002,0.00080002
103,42382,0.44735,0.39378,0.91381,0.76037,0.73623,0.78,0.69653,0.43668,0.42338,0.87169,0.00079804,0.00079804,0.00079804
104,42788.9,0.44689,0.39126,0.91246,0.74343,0.74803,0.77881,0.69503,0.43682,0.42335,0.87165,0.00079606,0.00079606,0.00079606
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 418.69 0.83535 1.13359 1.12916 0.58355 0.38296 0.40049 0.29072 0.85198 0.9464 1.0743 0.0670195 0.000333136 0.000333136
3 2 832.244 0.78027 0.98155 1.09921 0.55347 0.49267 0.49226 0.38532 0.73881 0.76766 1.01008 0.0340182 0.00066515 0.00066515
4 3 1245.36 0.7607 0.93537 1.0859 0.68314 0.48416 0.54006 0.42267 0.70499 0.71713 0.99057 0.00101557 0.000995844 0.000995844
5 4 1658.08 0.72934 0.88177 1.06589 0.68385 0.52174 0.55965 0.45334 0.64942 0.65696 0.95469 0.00099406 0.00099406 0.00099406
6 5 2071.43 0.69259 0.82831 1.04611 0.65451 0.55879 0.60955 0.49802 0.61654 0.5991 0.94472 0.00099208 0.00099208 0.00099208
7 6 2484.93 0.67132 0.79942 1.03475 0.63809 0.58909 0.64498 0.53898 0.58812 0.57445 0.93224 0.0009901 0.0009901 0.0009901
8 7 2897.63 0.65372 0.77202 1.02463 0.63581 0.63464 0.65829 0.55615 0.56852 0.5483 0.92767 0.00098812 0.00098812 0.00098812
9 8 3310.61 0.6412 0.74848 1.01886 0.66535 0.62042 0.6565 0.55715 0.54634 0.54175 0.91201 0.00098614 0.00098614 0.00098614
10 9 3723.44 0.62663 0.72925 1.01312 0.69047 0.6491 0.69149 0.58896 0.53693 0.51058 0.91197 0.00098416 0.00098416 0.00098416
11 10 4136.15 0.61641 0.71197 1.00549 0.65905 0.68377 0.70796 0.60279 0.52556 0.50095 0.90775 0.00098218 0.00098218 0.00098218
12 11 4549.15 0.60951 0.70123 1.00254 0.77429 0.63249 0.72609 0.62208 0.51859 0.49194 0.90477 0.0009802 0.0009802 0.0009802
13 12 4962.01 0.60036 0.68476 0.99667 0.69273 0.70661 0.7192 0.61669 0.51307 0.48322 0.89946 0.00097822 0.00097822 0.00097822
14 13 5374.93 0.59331 0.67326 0.99298 0.73702 0.69096 0.73417 0.63361 0.5005 0.4701 0.89502 0.00097624 0.00097624 0.00097624
15 14 5787.98 0.58571 0.66051 0.99001 0.73696 0.68337 0.73469 0.63523 0.49345 0.46298 0.89208 0.00097426 0.00097426 0.00097426
16 15 6200.77 0.58228 0.65152 0.98736 0.73404 0.69448 0.74113 0.64559 0.491 0.45165 0.89195 0.00097228 0.00097228 0.00097228
17 16 6613.85 0.57727 0.64241 0.98396 0.74017 0.69424 0.74893 0.65076 0.48925 0.44772 0.89293 0.0009703 0.0009703 0.0009703
18 17 7026.99 0.57085 0.63401 0.98151 0.75499 0.69208 0.75606 0.66141 0.48373 0.44486 0.88987 0.00096832 0.00096832 0.00096832
19 18 7445.77 0.56534 0.62296 0.978 0.74671 0.69906 0.75604 0.66082 0.48111 0.44055 0.88891 0.00096634 0.00096634 0.00096634
20 19 7858.49 0.56234 0.6184 0.97754 0.80391 0.67185 0.76344 0.66695 0.47915 0.43924 0.88837 0.00096436 0.00096436 0.00096436
21 20 8271.55 0.55967 0.61063 0.97563 0.7756 0.69819 0.76841 0.67156 0.47659 0.43795 0.88744 0.00096238 0.00096238 0.00096238
22 21 8687.45 0.55412 0.59943 0.96952 0.76666 0.70382 0.77489 0.67666 0.47599 0.43572 0.88665 0.0009604 0.0009604 0.0009604
23 22 9100.4 0.55025 0.59563 0.97045 0.80411 0.69054 0.77823 0.6801 0.47403 0.43469 0.8854 0.00095842 0.00095842 0.00095842
24 23 9513.34 0.54722 0.58926 0.96809 0.82337 0.68518 0.77857 0.68263 0.47328 0.43322 0.88533 0.00095644 0.00095644 0.00095644
25 24 9926.52 0.54295 0.58363 0.96584 0.80297 0.68982 0.78083 0.68564 0.47136 0.43154 0.8842 0.00095446 0.00095446 0.00095446
26 25 10339.4 0.53974 0.57646 0.96407 0.7699 0.70073 0.78134 0.68691 0.47007 0.43053 0.88364 0.00095248 0.00095248 0.00095248
27 26 10770.4 0.53886 0.57003 0.96211 0.73863 0.73039 0.78149 0.68724 0.46886 0.42886 0.88288 0.0009505 0.0009505 0.0009505
28 27 11203.6 0.53694 0.56648 0.96211 0.7222 0.74507 0.78238 0.68769 0.46738 0.42755 0.88182 0.00094852 0.00094852 0.00094852
29 28 11616.8 0.53234 0.55959 0.96062 0.72502 0.74511 0.78305 0.68868 0.46601 0.42598 0.88155 0.00094654 0.00094654 0.00094654
30 29 12029.6 0.53086 0.55749 0.95804 0.73893 0.73204 0.78328 0.6894 0.46503 0.42523 0.88086 0.00094456 0.00094456 0.00094456
31 30 12442.9 0.52916 0.55384 0.96024 0.75173 0.71998 0.78268 0.68942 0.46429 0.42449 0.88069 0.00094258 0.00094258 0.00094258
32 31 12856.1 0.52605 0.54554 0.95641 0.74954 0.73451 0.78353 0.68962 0.46374 0.42358 0.88029 0.0009406 0.0009406 0.0009406
33 32 13269.5 0.5259 0.54455 0.95655 0.74595 0.74918 0.78427 0.69016 0.46307 0.42256 0.87995 0.00093862 0.00093862 0.00093862
34 33 13682.7 0.52076 0.53968 0.95476 0.75051 0.75022 0.78912 0.69416 0.46215 0.42184 0.87972 0.00093664 0.00093664 0.00093664
35 34 14095.7 0.51925 0.53408 0.95289 0.76244 0.73852 0.79142 0.69639 0.46139 0.42097 0.87926 0.00093466 0.00093466 0.00093466
36 35 14508.7 0.51796 0.53128 0.95296 0.76476 0.74462 0.79161 0.69824 0.46064 0.42113 0.87856 0.00093268 0.00093268 0.00093268
37 36 14921.9 0.5146 0.52453 0.95148 0.76498 0.73775 0.79006 0.69735 0.45998 0.41975 0.87822 0.0009307 0.0009307 0.0009307
38 37 15334.5 0.51386 0.52272 0.95084 0.76097 0.74416 0.79441 0.70134 0.45932 0.41943 0.87796 0.00092872 0.00092872 0.00092872
39 38 15747.8 0.51141 0.51925 0.94828 0.76252 0.73061 0.79557 0.70253 0.45847 0.41881 0.87747 0.00092674 0.00092674 0.00092674
40 39 16160.9 0.51017 0.51475 0.94806 0.78945 0.71704 0.79672 0.70384 0.45772 0.41778 0.87718 0.00092476 0.00092476 0.00092476
41 40 16574.1 0.50964 0.51421 0.94737 0.78623 0.72013 0.79745 0.70409 0.45721 0.41756 0.87691 0.00092278 0.00092278 0.00092278
42 41 16987.3 0.50727 0.50681 0.94581 0.77233 0.72869 0.80081 0.70669 0.45646 0.41774 0.87651 0.0009208 0.0009208 0.0009208
43 42 17400.8 0.50444 0.50368 0.94476 0.7724 0.73005 0.80087 0.70645 0.45593 0.41723 0.87636 0.00091882 0.00091882 0.00091882
44 43 17817.2 0.50331 0.50043 0.94272 0.78011 0.72365 0.80025 0.70637 0.45531 0.41667 0.87577 0.00091684 0.00091684 0.00091684
45 44 18230.2 0.49997 0.49976 0.94246 0.76128 0.73765 0.79881 0.70533 0.45447 0.4164 0.87496 0.00091486 0.00091486 0.00091486
46 45 18643.5 0.50128 0.49564 0.94259 0.80039 0.71037 0.80016 0.7073 0.45293 0.4157 0.8741 0.00091288 0.00091288 0.00091288
47 46 19057 0.4993 0.49319 0.94164 0.76954 0.73201 0.79752 0.70481 0.45216 0.41622 0.87362 0.0009109 0.0009109 0.0009109
48 47 19470 0.49727 0.49241 0.94068 0.7627 0.74122 0.79872 0.70583 0.45167 0.41558 0.8735 0.00090892 0.00090892 0.00090892
49 48 19883.1 0.49796 0.48956 0.94143 0.76406 0.73882 0.79846 0.70615 0.45138 0.41527 0.87353 0.00090694 0.00090694 0.00090694
50 49 20296.5 0.49546 0.48413 0.94004 0.77804 0.73915 0.79651 0.7049 0.45036 0.41469 0.873 0.00090496 0.00090496 0.00090496
51 50 20709.9 0.49374 0.48193 0.93856 0.75715 0.74998 0.79761 0.70672 0.44862 0.41322 0.87172 0.00090298 0.00090298 0.00090298
52 51 21123 0.49173 0.48042 0.93761 0.75312 0.76114 0.80086 0.70989 0.4478 0.41251 0.87141 0.000901 0.000901 0.000901
53 52 21536.1 0.4925 0.47896 0.93668 0.75894 0.75716 0.79916 0.70979 0.44683 0.41208 0.8711 0.00089902 0.00089902 0.00089902
54 53 21949.3 0.49059 0.47373 0.93603 0.75033 0.75753 0.79993 0.71 0.44632 0.41236 0.87075 0.00089704 0.00089704 0.00089704
55 54 22362.7 0.48891 0.47249 0.93646 0.74364 0.76568 0.79959 0.71227 0.44535 0.41234 0.87075 0.00089506 0.00089506 0.00089506
56 55 22776.2 0.48682 0.46793 0.93362 0.74023 0.76995 0.79986 0.71232 0.44468 0.41223 0.87038 0.00089308 0.00089308 0.00089308
57 56 23189.5 0.48631 0.46959 0.93358 0.75056 0.76026 0.80061 0.71176 0.44416 0.4118 0.8703 0.0008911 0.0008911 0.0008911
58 57 23607.7 0.48619 0.46501 0.93519 0.7537 0.76494 0.79862 0.71104 0.44394 0.41235 0.87053 0.00088912 0.00088912 0.00088912
59 58 24020.7 0.48476 0.46277 0.93324 0.75743 0.76102 0.79932 0.71131 0.44358 0.41228 0.87041 0.00088714 0.00088714 0.00088714
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fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/args.yaml
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task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_bak.yaml
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time: null
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105
fashion_yolo/fashionpedia_exp/yolov8m_fashion_all/results.csv
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54,22342,0.73314,0.62566,1.00821,0.61985,0.56195,0.59067,0.47266,0.73331,0.61148,0.99418,0.0008251,0.0008251,0.0008251
55,22752.4,0.73011,0.61848,1.00486,0.62353,0.56161,0.58978,0.471,0.73333,0.61089,0.99429,0.0008218,0.0008218,0.0008218
56,23162.5,0.72865,0.61946,1.00492,0.63636,0.55107,0.58775,0.47005,0.733,0.61004,0.99443,0.0008185,0.0008185,0.0008185
57,23572.6,0.72751,0.61457,1.00303,0.63114,0.55687,0.58844,0.47019,0.73279,0.60992,0.99432,0.0008152,0.0008152,0.0008152
58,23982.7,0.72573,0.61133,1.0021,0.60145,0.56762,0.58748,0.46936,0.73232,0.60943,0.99384,0.0008119,0.0008119,0.0008119
59,24392.9,0.72552,0.6103,1.00162,0.60527,0.56645,0.5875,0.46907,0.73265,0.60934,0.99406,0.0008086,0.0008086,0.0008086
60,24803.3,0.72448,0.60779,1.00225,0.59358,0.57213,0.58675,0.46968,0.73268,0.60956,0.99438,0.0008053,0.0008053,0.0008053
61,25213.3,0.72201,0.60537,1.0006,0.60078,0.57007,0.58518,0.46926,0.733,0.60976,0.99469,0.000802,0.000802,0.000802
62,25623.4,0.72112,0.60085,1.00023,0.60742,0.56656,0.58485,0.46913,0.73274,0.61003,0.99466,0.0007987,0.0007987,0.0007987
63,26033.5,0.71758,0.60017,0.99875,0.59854,0.56748,0.58636,0.46925,0.73258,0.61016,0.99463,0.0007954,0.0007954,0.0007954
64,26443.8,0.71715,0.59841,0.99904,0.59846,0.56616,0.58669,0.46959,0.73231,0.61029,0.99474,0.0007921,0.0007921,0.0007921
65,26853.8,0.71691,0.59505,0.9978,0.60926,0.56065,0.58485,0.4673,0.73209,0.6109,0.99473,0.0007888,0.0007888,0.0007888
66,27264.1,0.71677,0.59351,0.99611,0.60223,0.56804,0.58475,0.46656,0.73187,0.61034,0.9945,0.0007855,0.0007855,0.0007855
67,27674.1,0.71316,0.59057,0.99427,0.59928,0.56693,0.58358,0.46656,0.73204,0.61015,0.99485,0.0007822,0.0007822,0.0007822
68,28084.2,0.71189,0.58878,0.99407,0.61673,0.55507,0.58163,0.46478,0.73195,0.61036,0.99488,0.0007789,0.0007789,0.0007789
69,28494.1,0.71066,0.58626,0.99383,0.60563,0.56086,0.58158,0.46373,0.73211,0.61065,0.9951,0.0007756,0.0007756,0.0007756
70,28904.1,0.71027,0.5854,0.99279,0.60943,0.55783,0.57942,0.46335,0.73193,0.61148,0.99527,0.0007723,0.0007723,0.0007723
71,29314,0.71042,0.58246,0.99383,0.60752,0.55747,0.57731,0.46233,0.73202,0.61195,0.99545,0.000769,0.000769,0.000769
72,29724.1,0.70936,0.58204,0.99266,0.6014,0.55801,0.57716,0.46119,0.73121,0.61196,0.99488,0.0007657,0.0007657,0.0007657
73,30134.3,0.70641,0.57848,0.99147,0.60018,0.56144,0.57629,0.461,0.73054,0.61204,0.99449,0.0007624,0.0007624,0.0007624
74,30544.1,0.70596,0.57837,0.99054,0.59549,0.56442,0.57589,0.46084,0.72959,0.61257,0.99409,0.0007591,0.0007591,0.0007591
75,30954.3,0.70412,0.57449,0.98966,0.59922,0.56204,0.57362,0.45879,0.72944,0.61322,0.99398,0.0007558,0.0007558,0.0007558
76,31364.2,0.70301,0.57244,0.99015,0.60298,0.55752,0.57468,0.46117,0.72919,0.61276,0.99405,0.0007525,0.0007525,0.0007525
77,31774.2,0.7023,0.57249,0.98927,0.60336,0.55564,0.57274,0.45911,0.72905,0.61349,0.99431,0.0007492,0.0007492,0.0007492
78,32183.9,0.70131,0.57191,0.98921,0.59972,0.55688,0.57172,0.45901,0.7285,0.61357,0.99442,0.0007459,0.0007459,0.0007459
79,32593.9,0.6996,0.56764,0.9882,0.60475,0.55516,0.57241,0.45872,0.72866,0.61347,0.99426,0.0007426,0.0007426,0.0007426
80,33003.9,0.69797,0.56562,0.98792,0.60385,0.55107,0.57159,0.45846,0.72914,0.6139,0.99445,0.0007393,0.0007393,0.0007393
81,33413.9,0.69656,0.56416,0.98596,0.60748,0.54796,0.57153,0.45958,0.72982,0.61457,0.99502,0.000736,0.000736,0.000736
82,33823.6,0.69523,0.56022,0.9856,0.60131,0.54144,0.57152,0.45858,0.73037,0.61492,0.99571,0.0007327,0.0007327,0.0007327
83,34233.9,0.69415,0.55999,0.98587,0.59313,0.54856,0.57215,0.45822,0.73065,0.61518,0.99636,0.0007294,0.0007294,0.0007294
84,34643.6,0.69417,0.55917,0.98452,0.5951,0.54482,0.57584,0.46094,0.73097,0.61567,0.99643,0.0007261,0.0007261,0.0007261
85,35053.5,0.69462,0.55972,0.98437,0.5968,0.54578,0.5741,0.45978,0.7308,0.61608,0.99627,0.0007228,0.0007228,0.0007228
86,35463.4,0.69211,0.55473,0.98365,0.60573,0.54224,0.57294,0.45987,0.73042,0.61601,0.99608,0.0007195,0.0007195,0.0007195
87,35873.1,0.69119,0.55473,0.98453,0.62068,0.5373,0.57315,0.4601,0.73024,0.61575,0.99599,0.0007162,0.0007162,0.0007162
88,36283.3,0.68924,0.55485,0.98278,0.6149,0.53637,0.57243,0.45938,0.73053,0.61529,0.99653,0.0007129,0.0007129,0.0007129
89,36693.2,0.6881,0.54932,0.98155,0.61216,0.53666,0.57275,0.45942,0.73066,0.61513,0.99701,0.0007096,0.0007096,0.0007096
90,37103.4,0.68748,0.54711,0.9811,0.61754,0.53869,0.57284,0.46038,0.73081,0.61537,0.99721,0.0007063,0.0007063,0.0007063
91,37513.3,0.68639,0.54586,0.9802,0.61986,0.53636,0.573,0.46059,0.73082,0.61475,0.99741,0.000703,0.000703,0.000703
92,37923.2,0.6858,0.54607,0.98105,0.6199,0.53775,0.57223,0.45978,0.73125,0.61554,0.99742,0.0006997,0.0006997,0.0006997
93,38332.9,0.68458,0.5445,0.98017,0.62475,0.53637,0.57131,0.4599,0.73125,0.61601,0.99765,0.0006964,0.0006964,0.0006964
94,38742.8,0.68476,0.54496,0.97901,0.62742,0.53615,0.57047,0.45943,0.73138,0.61591,0.99757,0.0006931,0.0006931,0.0006931
95,39152.9,0.68467,0.54329,0.97949,0.62647,0.53598,0.56967,0.45891,0.73135,0.61586,0.99786,0.0006898,0.0006898,0.0006898
96,39562.7,0.68248,0.53889,0.97827,0.63125,0.53332,0.56914,0.45883,0.73137,0.61569,0.99801,0.0006865,0.0006865,0.0006865
97,39972.4,0.68149,0.53843,0.97811,0.63182,0.53225,0.56928,0.45931,0.73212,0.61574,0.99904,0.0006832,0.0006832,0.0006832
98,40382.5,0.67966,0.53569,0.97732,0.63148,0.53334,0.56919,0.45914,0.73285,0.61689,1.0001,0.0006799,0.0006799,0.0006799
99,40792.6,0.67831,0.53489,0.9763,0.63827,0.53174,0.56748,0.45676,0.7333,0.61708,1.00088,0.0006766,0.0006766,0.0006766
100,41202.4,0.67859,0.53438,0.97539,0.64537,0.52882,0.56857,0.45781,0.73349,0.61774,1.00156,0.0006733,0.0006733,0.0006733
101,41612,0.67698,0.53195,0.97421,0.59689,0.55476,0.56698,0.45707,0.73428,0.61818,1.0023,0.00067,0.00067,0.00067
102,42021.9,0.67614,0.52984,0.97443,0.64668,0.52583,0.56655,0.4573,0.73411,0.61871,1.00209,0.0006667,0.0006667,0.0006667
103,42431.9,0.67603,0.53043,0.97495,0.59965,0.5591,0.56899,0.45881,0.73429,0.61872,1.00227,0.0006634,0.0006634,0.0006634
104,42842.1,0.67442,0.52656,0.97396,0.65822,0.5248,0.56951,0.45846,0.73465,0.61952,1.00245,0.0006601,0.0006601,0.0006601
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 423.272 1.11804 1.48533 1.24553 0.55 0.26673 0.25707 0.17107 1.16457 1.42309 1.25638 0.0670202 0.000333129 0.000333129
3 2 840.821 1.07037 1.25643 1.21377 0.53272 0.34092 0.35016 0.2431 1.03132 1.08857 1.17762 0.034018 0.000664263 0.000664263
4 3 1257.38 1.04423 1.18909 1.19634 0.58003 0.35043 0.36787 0.26313 0.99679 1.02316 1.15343 0.00101365 0.000993197 0.000993197
5 4 1673.82 1.00647 1.11537 1.17171 0.55406 0.39638 0.40526 0.2965 0.94548 0.93585 1.11311 0.0009901 0.0009901 0.0009901
6 5 2090.38 0.97138 1.05029 1.15031 0.59595 0.40732 0.43596 0.32105 0.90757 0.89195 1.09525 0.0009868 0.0009868 0.0009868
7 6 2506.59 0.94497 1.00603 1.13383 0.64868 0.40786 0.44323 0.33174 0.88859 0.84335 1.08578 0.0009835 0.0009835 0.0009835
8 7 2923.08 0.92564 0.97481 1.12234 0.65689 0.42494 0.4649 0.3534 0.85642 0.8164 1.06716 0.0009802 0.0009802 0.0009802
9 8 3339.6 0.90909 0.94347 1.11224 0.63848 0.42443 0.47592 0.35961 0.8487 0.78128 1.05486 0.0009769 0.0009769 0.0009769
10 9 3756.01 0.89516 0.91892 1.10343 0.69973 0.44769 0.49843 0.38387 0.82817 0.76073 1.04908 0.0009736 0.0009736 0.0009736
11 10 4172.3 0.88575 0.90029 1.09692 0.61612 0.47757 0.49803 0.38389 0.81523 0.74462 1.03997 0.0009703 0.0009703 0.0009703
12 11 4588.8 0.87504 0.88374 1.09055 0.66866 0.46711 0.50611 0.38849 0.80635 0.72838 1.03532 0.000967 0.000967 0.000967
13 12 5005.39 0.86584 0.86517 1.08645 0.66044 0.47761 0.51259 0.39859 0.80069 0.71077 1.03119 0.0009637 0.0009637 0.0009637
14 13 5421.27 0.85928 0.85385 1.0815 0.64495 0.48769 0.52093 0.40611 0.79041 0.70016 1.02656 0.0009604 0.0009604 0.0009604
15 14 5837.21 0.85239 0.83961 1.07615 0.65564 0.4919 0.52842 0.41299 0.78587 0.69185 1.02196 0.0009571 0.0009571 0.0009571
16 15 6253.65 0.84563 0.82737 1.07137 0.56095 0.52905 0.53188 0.41613 0.78111 0.68483 1.0185 0.0009538 0.0009538 0.0009538
17 16 6670.18 0.84027 0.81615 1.06991 0.61213 0.51121 0.53901 0.42027 0.77819 0.67904 1.01714 0.0009505 0.0009505 0.0009505
18 17 7086.54 0.83301 0.8069 1.06665 0.61937 0.52332 0.54373 0.42679 0.77574 0.67352 1.01628 0.0009472 0.0009472 0.0009472
19 18 7503.23 0.82938 0.79701 1.06196 0.60429 0.54006 0.54873 0.43032 0.77035 0.6686 1.01274 0.0009439 0.0009439 0.0009439
20 19 7920.06 0.82429 0.78797 1.05985 0.60365 0.53738 0.54911 0.43311 0.76634 0.66542 1.0101 0.0009406 0.0009406 0.0009406
21 20 8340.15 0.81808 0.77603 1.05516 0.62821 0.52777 0.55339 0.43479 0.76488 0.65991 1.00914 0.0009373 0.0009373 0.0009373
22 21 8756.4 0.81396 0.76921 1.05319 0.59172 0.54578 0.55009 0.43164 0.76265 0.65798 1.00884 0.000934 0.000934 0.000934
23 22 9172.6 0.81014 0.76035 1.05008 0.59012 0.53698 0.54707 0.43129 0.76103 0.65588 1.00833 0.0009307 0.0009307 0.0009307
24 23 9588.51 0.80568 0.75486 1.04971 0.60168 0.53635 0.55145 0.43471 0.75911 0.65384 1.00728 0.0009274 0.0009274 0.0009274
25 24 10004.7 0.80139 0.74618 1.04613 0.59958 0.53801 0.55326 0.43806 0.75714 0.65129 1.00581 0.0009241 0.0009241 0.0009241
26 25 10420.9 0.7978 0.74211 1.04536 0.62449 0.53635 0.55474 0.43831 0.75631 0.64906 1.00497 0.0009208 0.0009208 0.0009208
27 26 10837.4 0.79528 0.73449 1.04164 0.63605 0.53384 0.55552 0.44099 0.75488 0.64754 1.00395 0.0009175 0.0009175 0.0009175
28 27 11253.5 0.79075 0.72837 1.0388 0.63987 0.53375 0.55397 0.44004 0.75382 0.64507 1.00302 0.0009142 0.0009142 0.0009142
29 28 11669.8 0.78667 0.72317 1.03767 0.6355 0.54046 0.55626 0.44196 0.75263 0.6427 1.00235 0.0009109 0.0009109 0.0009109
30 29 12085.9 0.7866 0.7209 1.03741 0.64041 0.54434 0.55837 0.44339 0.75214 0.64107 1.00195 0.0009076 0.0009076 0.0009076
31 30 12496.2 0.78208 0.71487 1.03461 0.63898 0.54922 0.55991 0.44429 0.75153 0.63903 1.00183 0.0009043 0.0009043 0.0009043
32 31 12906.7 0.78115 0.71002 1.03384 0.64412 0.54747 0.56224 0.4454 0.75125 0.63683 1.00159 0.000901 0.000901 0.000901
33 32 13317 0.77699 0.70108 1.03151 0.65986 0.53939 0.567 0.44978 0.75026 0.6357 1.00106 0.0008977 0.0008977 0.0008977
34 33 13727.3 0.77535 0.69913 1.03055 0.64206 0.56155 0.57 0.45296 0.74904 0.63426 1.00034 0.0008944 0.0008944 0.0008944
35 34 14137.7 0.77129 0.69369 1.02894 0.63568 0.55759 0.57311 0.45488 0.74735 0.63236 0.99948 0.0008911 0.0008911 0.0008911
36 35 14548.1 0.76992 0.68841 1.02856 0.6324 0.56448 0.57376 0.45633 0.74607 0.63006 0.99897 0.0008878 0.0008878 0.0008878
37 36 14958.2 0.76593 0.68384 1.02638 0.62909 0.5638 0.57579 0.45877 0.7451 0.62879 0.99878 0.0008845 0.0008845 0.0008845
38 37 15368.3 0.76445 0.68161 1.02471 0.60819 0.55986 0.5756 0.45867 0.74458 0.62793 0.99885 0.0008812 0.0008812 0.0008812
39 38 15778.5 0.76251 0.67843 1.02456 0.61289 0.56002 0.57464 0.45763 0.74355 0.62695 0.99828 0.0008779 0.0008779 0.0008779
40 39 16188.9 0.7592 0.67096 1.02194 0.61614 0.55895 0.5756 0.45853 0.7425 0.62544 0.9977 0.0008746 0.0008746 0.0008746
41 40 16599 0.75831 0.66992 1.02141 0.61776 0.55886 0.57776 0.46098 0.74138 0.62379 0.99725 0.0008713 0.0008713 0.0008713
42 41 17009.6 0.75731 0.66643 1.02147 0.62515 0.5579 0.5781 0.46186 0.74017 0.62236 0.99673 0.000868 0.000868 0.000868
43 42 17420 0.75458 0.66212 1.01902 0.62978 0.55591 0.57953 0.4631 0.73897 0.62079 0.99648 0.0008647 0.0008647 0.0008647
44 43 17830.4 0.75223 0.65784 1.01701 0.63946 0.55466 0.58026 0.46272 0.73825 0.61965 0.99621 0.0008614 0.0008614 0.0008614
45 44 18240.7 0.7493 0.65385 1.01597 0.63252 0.55549 0.58281 0.46439 0.73794 0.61861 0.99623 0.0008581 0.0008581 0.0008581
46 45 18650.9 0.74636 0.64901 1.01488 0.63223 0.55666 0.58385 0.46423 0.73778 0.6177 0.9961 0.0008548 0.0008548 0.0008548
47 46 19061 0.74588 0.64735 1.01344 0.63236 0.55954 0.58421 0.46472 0.73799 0.61658 0.99655 0.0008515 0.0008515 0.0008515
48 47 19471.2 0.74496 0.64645 1.01411 0.62892 0.56955 0.58505 0.46638 0.73712 0.61578 0.99604 0.0008482 0.0008482 0.0008482
49 48 19881.5 0.74294 0.64192 1.01247 0.62847 0.57009 0.58727 0.46842 0.73682 0.61516 0.99616 0.0008449 0.0008449 0.0008449
50 49 20291.7 0.74067 0.63988 1.01018 0.65109 0.56945 0.5872 0.46858 0.73712 0.61464 0.99658 0.0008416 0.0008416 0.0008416
51 50 20701.7 0.73754 0.63558 1.00985 0.65764 0.56653 0.5878 0.47019 0.73676 0.6137 0.99644 0.0008383 0.0008383 0.0008383
52 51 21111.8 0.73674 0.63168 1.00902 0.63906 0.55753 0.58842 0.47075 0.73661 0.61332 0.99618 0.000835 0.000835 0.000835
53 52 21522 0.73633 0.62923 1.00848 0.63235 0.56754 0.58923 0.47078 0.73546 0.61228 0.99531 0.0008317 0.0008317 0.0008317
54 53 21932 0.73376 0.62668 1.00679 0.61277 0.56915 0.58976 0.47102 0.73427 0.61174 0.99452 0.0008284 0.0008284 0.0008284
55 54 22342 0.73314 0.62566 1.00821 0.61985 0.56195 0.59067 0.47266 0.73331 0.61148 0.99418 0.0008251 0.0008251 0.0008251
56 55 22752.4 0.73011 0.61848 1.00486 0.62353 0.56161 0.58978 0.471 0.73333 0.61089 0.99429 0.0008218 0.0008218 0.0008218
57 56 23162.5 0.72865 0.61946 1.00492 0.63636 0.55107 0.58775 0.47005 0.733 0.61004 0.99443 0.0008185 0.0008185 0.0008185
58 57 23572.6 0.72751 0.61457 1.00303 0.63114 0.55687 0.58844 0.47019 0.73279 0.60992 0.99432 0.0008152 0.0008152 0.0008152
59 58 23982.7 0.72573 0.61133 1.0021 0.60145 0.56762 0.58748 0.46936 0.73232 0.60943 0.99384 0.0008119 0.0008119 0.0008119
60 59 24392.9 0.72552 0.6103 1.00162 0.60527 0.56645 0.5875 0.46907 0.73265 0.60934 0.99406 0.0008086 0.0008086 0.0008086
61 60 24803.3 0.72448 0.60779 1.00225 0.59358 0.57213 0.58675 0.46968 0.73268 0.60956 0.99438 0.0008053 0.0008053 0.0008053
62 61 25213.3 0.72201 0.60537 1.0006 0.60078 0.57007 0.58518 0.46926 0.733 0.60976 0.99469 0.000802 0.000802 0.000802
63 62 25623.4 0.72112 0.60085 1.00023 0.60742 0.56656 0.58485 0.46913 0.73274 0.61003 0.99466 0.0007987 0.0007987 0.0007987
64 63 26033.5 0.71758 0.60017 0.99875 0.59854 0.56748 0.58636 0.46925 0.73258 0.61016 0.99463 0.0007954 0.0007954 0.0007954
65 64 26443.8 0.71715 0.59841 0.99904 0.59846 0.56616 0.58669 0.46959 0.73231 0.61029 0.99474 0.0007921 0.0007921 0.0007921
66 65 26853.8 0.71691 0.59505 0.9978 0.60926 0.56065 0.58485 0.4673 0.73209 0.6109 0.99473 0.0007888 0.0007888 0.0007888
67 66 27264.1 0.71677 0.59351 0.99611 0.60223 0.56804 0.58475 0.46656 0.73187 0.61034 0.9945 0.0007855 0.0007855 0.0007855
68 67 27674.1 0.71316 0.59057 0.99427 0.59928 0.56693 0.58358 0.46656 0.73204 0.61015 0.99485 0.0007822 0.0007822 0.0007822
69 68 28084.2 0.71189 0.58878 0.99407 0.61673 0.55507 0.58163 0.46478 0.73195 0.61036 0.99488 0.0007789 0.0007789 0.0007789
70 69 28494.1 0.71066 0.58626 0.99383 0.60563 0.56086 0.58158 0.46373 0.73211 0.61065 0.9951 0.0007756 0.0007756 0.0007756
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fashion_yolo/fashionpedia_exp/yolov8m_fashion_final/args.yaml
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task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_reduced.yaml
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time: null
patience: 50
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108
fashion_yolo/fashionpedia_exp/yolov8m_fashion_final/results.csv
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63,28450.5,0.54489,0.50109,0.96692,0.76253,0.74972,0.78137,0.68431,0.53442,0.50701,0.94117,0.00087724,0.00087724,0.00087724
64,28901.9,0.54279,0.49894,0.96659,0.7633,0.74753,0.78198,0.68422,0.534,0.50808,0.94112,0.00087526,0.00087526,0.00087526
65,29353.6,0.5446,0.49924,0.96593,0.76671,0.75031,0.78116,0.68332,0.53472,0.50707,0.94103,0.00087328,0.00087328,0.00087328
66,29804.2,0.54173,0.49441,0.96263,0.76454,0.75281,0.78041,0.68392,0.53482,0.5069,0.94137,0.0008713,0.0008713,0.0008713
67,30255.2,0.53911,0.49062,0.96278,0.76411,0.75269,0.78029,0.68218,0.53491,0.50599,0.94152,0.00086932,0.00086932,0.00086932
68,30706.1,0.53856,0.48794,0.96289,0.76645,0.75188,0.78076,0.68253,0.53421,0.50596,0.94114,0.00086734,0.00086734,0.00086734
69,31157.4,0.53968,0.48909,0.96455,0.76773,0.74754,0.78074,0.68375,0.53362,0.50555,0.94038,0.00086536,0.00086536,0.00086536
70,31608.5,0.53628,0.48699,0.96154,0.77422,0.74093,0.77942,0.68391,0.53369,0.50468,0.94026,0.00086338,0.00086338,0.00086338
71,32060.1,0.53537,0.48481,0.96135,0.78093,0.73938,0.77988,0.68439,0.53398,0.50439,0.94033,0.0008614,0.0008614,0.0008614
72,32510.8,0.53519,0.48424,0.96144,0.77374,0.7459,0.78217,0.6873,0.53426,0.50416,0.94064,0.00085942,0.00085942,0.00085942
73,32962,0.53448,0.48211,0.96052,0.76365,0.74936,0.78153,0.68739,0.53397,0.50588,0.94086,0.00085744,0.00085744,0.00085744
74,33413.1,0.53141,0.47597,0.95823,0.76587,0.74857,0.7824,0.68773,0.53435,0.50584,0.94125,0.00085546,0.00085546,0.00085546
75,33864.1,0.53026,0.47387,0.95857,0.77682,0.7289,0.78131,0.68765,0.53429,0.50494,0.94158,0.00085348,0.00085348,0.00085348
76,34315,0.52914,0.4735,0.95905,0.77719,0.73672,0.78165,0.68694,0.534,0.50556,0.94164,0.0008515,0.0008515,0.0008515
77,34766,0.53026,0.47048,0.95843,0.7549,0.75072,0.78287,0.68702,0.53376,0.50522,0.94169,0.00084952,0.00084952,0.00084952
78,35216.7,0.52681,0.47115,0.95676,0.75612,0.7557,0.78295,0.68559,0.53249,0.50593,0.94083,0.00084754,0.00084754,0.00084754
79,35667.8,0.52582,0.46752,0.95797,0.75908,0.7579,0.781,0.68412,0.53292,0.50724,0.94153,0.00084556,0.00084556,0.00084556
80,36118.6,0.52694,0.46825,0.95616,0.749,0.7733,0.78172,0.68458,0.53265,0.50986,0.9416,0.00084358,0.00084358,0.00084358
81,36570.1,0.52658,0.46707,0.95675,0.74576,0.76459,0.78112,0.68344,0.5338,0.51163,0.94245,0.0008416,0.0008416,0.0008416
82,37021.7,0.52438,0.46259,0.95575,0.73804,0.78133,0.78056,0.68356,0.53479,0.51276,0.94353,0.00083962,0.00083962,0.00083962
83,37473.3,0.5246,0.46244,0.95754,0.74546,0.77143,0.77955,0.68195,0.535,0.51347,0.94416,0.00083764,0.00083764,0.00083764
84,37924.5,0.5228,0.45967,0.95448,0.7492,0.76876,0.77896,0.68264,0.53582,0.51257,0.94461,0.00083566,0.00083566,0.00083566
85,38376,0.52085,0.45942,0.9541,0.74132,0.78106,0.77977,0.68229,0.5361,0.51318,0.94447,0.00083368,0.00083368,0.00083368
86,38827.5,0.52124,0.45523,0.95424,0.73883,0.77121,0.78252,0.68506,0.53639,0.5152,0.94492,0.0008317,0.0008317,0.0008317
87,39279.1,0.5171,0.45303,0.95061,0.76468,0.75787,0.7794,0.68039,0.53727,0.51576,0.9456,0.00082972,0.00082972,0.00082972
88,39730.2,0.52005,0.45384,0.95344,0.76706,0.75256,0.77996,0.68002,0.53756,0.51479,0.94616,0.00082774,0.00082774,0.00082774
89,40181.8,0.51805,0.45267,0.95219,0.76351,0.7541,0.78124,0.68107,0.53802,0.51426,0.94696,0.00082576,0.00082576,0.00082576
90,40633.1,0.51703,0.45019,0.95272,0.73783,0.77513,0.78246,0.68086,0.53837,0.51332,0.94776,0.00082378,0.00082378,0.00082378
91,41084.8,0.51559,0.44851,0.95219,0.81122,0.7152,0.78376,0.6829,0.5393,0.5135,0.94856,0.0008218,0.0008218,0.0008218
92,41535.9,0.51465,0.44659,0.95235,0.807,0.71948,0.7853,0.68338,0.54013,0.51449,0.94903,0.00081982,0.00081982,0.00081982
93,41986.8,0.51449,0.44483,0.95123,0.74369,0.77954,0.78389,0.68213,0.54058,0.51453,0.94929,0.00081784,0.00081784,0.00081784
94,42438.8,0.51396,0.4426,0.94942,0.75636,0.77138,0.78564,0.68528,0.5398,0.51464,0.94925,0.00081586,0.00081586,0.00081586
95,42889.9,0.51486,0.44344,0.95128,0.75353,0.76984,0.78619,0.68502,0.53954,0.51507,0.94948,0.00081388,0.00081388,0.00081388
96,43341.1,0.51151,0.44189,0.94857,0.75198,0.77322,0.78581,0.68452,0.54018,0.5163,0.95016,0.0008119,0.0008119,0.0008119
97,43792.6,0.50917,0.43834,0.94702,0.77034,0.75353,0.78541,0.68504,0.53944,0.51656,0.95005,0.00080992,0.00080992,0.00080992
98,44243.9,0.51041,0.43968,0.94815,0.7627,0.76859,0.78607,0.68568,0.53983,0.51754,0.95151,0.00080794,0.00080794,0.00080794
99,44695.2,0.50957,0.43676,0.94735,0.75896,0.76465,0.78632,0.68583,0.53844,0.51808,0.95059,0.00080596,0.00080596,0.00080596
100,45146.3,0.50745,0.43537,0.94694,0.75391,0.77257,0.78599,0.68651,0.53775,0.51787,0.95023,0.00080398,0.00080398,0.00080398
101,45597.5,0.50812,0.43582,0.94704,0.77745,0.75832,0.78675,0.68662,0.53684,0.51773,0.95023,0.000802,0.000802,0.000802
102,46048.1,0.50562,0.43169,0.94502,0.78022,0.75828,0.78581,0.68707,0.53733,0.51798,0.95139,0.00080002,0.00080002,0.00080002
103,46499.1,0.50575,0.43067,0.94398,0.75636,0.77235,0.7825,0.68496,0.5371,0.51874,0.95146,0.00079804,0.00079804,0.00079804
104,46949.4,0.50372,0.42811,0.94322,0.76878,0.75628,0.78152,0.68417,0.53661,0.51882,0.95104,0.00079606,0.00079606,0.00079606
105,47400.5,0.50518,0.4299,0.94321,0.80925,0.7268,0.78229,0.68461,0.5364,0.5197,0.95073,0.00079408,0.00079408,0.00079408
106,47851.8,0.50258,0.42721,0.94219,0.77003,0.75321,0.78269,0.68599,0.5354,0.52134,0.94919,0.0007921,0.0007921,0.0007921
107,48302.5,0.50185,0.4262,0.94187,0.77956,0.74669,0.78216,0.68629,0.53593,0.52174,0.94943,0.00079012,0.00079012,0.00079012
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 457.89 0.88683 1.32931 1.18557 0.51731 0.44505 0.44618 0.32256 0.95622 1.24877 1.19403 0.067034 0.00033299 0.00033299
3 2 916.415 0.918 1.22373 1.19908 0.51802 0.45856 0.47624 0.35536 0.90873 1.08857 1.17258 0.0340327 0.000665004 0.000665004
4 3 1375.07 0.89471 1.15842 1.18234 0.59881 0.49085 0.52052 0.39983 0.8716 0.98792 1.13218 0.00103003 0.000995698 0.000995698
5 4 1829.35 0.84385 1.06735 1.15031 0.58293 0.5494 0.56973 0.44688 0.78717 0.86602 1.08828 0.00099406 0.00099406 0.00099406
6 5 2280.57 0.80122 0.99233 1.12344 0.64024 0.58091 0.61291 0.49565 0.7324 0.78198 1.05296 0.00099208 0.00099208 0.00099208
7 6 2732.37 0.77294 0.94658 1.10477 0.58446 0.61699 0.63157 0.51447 0.70957 0.73479 1.03662 0.0009901 0.0009901 0.0009901
8 7 3183.72 0.75144 0.91401 1.09045 0.68525 0.61853 0.66465 0.54695 0.67567 0.69362 1.0173 0.00098812 0.00098812 0.00098812
9 8 3634.19 0.73716 0.88066 1.08194 0.69799 0.63739 0.68292 0.56642 0.66128 0.66836 1.01048 0.00098614 0.00098614 0.00098614
10 9 4085.8 0.72163 0.85649 1.07271 0.73886 0.62402 0.6918 0.57915 0.63789 0.64143 0.99556 0.00098416 0.00098416 0.00098416
11 10 4537.19 0.7122 0.83631 1.06702 0.73234 0.65818 0.7105 0.59646 0.62256 0.61749 0.98779 0.00098218 0.00098218 0.00098218
12 11 4988.92 0.69997 0.81747 1.05917 0.71541 0.66997 0.72246 0.61203 0.61202 0.60238 0.97674 0.0009802 0.0009802 0.0009802
13 12 5440.49 0.69099 0.79945 1.05375 0.73632 0.67777 0.73404 0.62541 0.59816 0.58309 0.97069 0.00097822 0.00097822 0.00097822
14 13 5892.2 0.68417 0.7835 1.05059 0.71903 0.68948 0.73839 0.6316 0.58965 0.57025 0.97066 0.00097624 0.00097624 0.00097624
15 14 6343.54 0.67596 0.77427 1.04726 0.78736 0.66938 0.74799 0.64299 0.58801 0.56243 0.97139 0.00097426 0.00097426 0.00097426
16 15 6794.76 0.67035 0.75674 1.04228 0.78497 0.6816 0.74962 0.64266 0.58505 0.55707 0.96724 0.00097228 0.00097228 0.00097228
17 16 7245.86 0.66228 0.74541 1.03696 0.78396 0.6861 0.75294 0.64713 0.57994 0.55312 0.96355 0.0009703 0.0009703 0.0009703
18 17 7697.5 0.65762 0.72954 1.03504 0.78443 0.69864 0.75598 0.65041 0.57789 0.54772 0.96355 0.00096832 0.00096832 0.00096832
19 18 8148.76 0.65246 0.72311 1.03197 0.77804 0.71214 0.76245 0.65771 0.57606 0.54191 0.96332 0.00096634 0.00096634 0.00096634
20 19 8599.95 0.64532 0.70783 1.02904 0.78333 0.71374 0.76433 0.65953 0.57378 0.53887 0.96165 0.00096436 0.00096436 0.00096436
21 20 9051.33 0.64054 0.69728 1.02377 0.79915 0.70741 0.76972 0.66552 0.57205 0.53533 0.96076 0.00096238 0.00096238 0.00096238
22 21 9502.62 0.63734 0.69125 1.02201 0.79518 0.71025 0.77018 0.66649 0.57049 0.53383 0.95993 0.0009604 0.0009604 0.0009604
23 22 9953.79 0.63331 0.68273 1.02055 0.79763 0.71238 0.77075 0.66661 0.56907 0.53096 0.95902 0.00095842 0.00095842 0.00095842
24 23 10405 0.62767 0.67424 1.0177 0.79869 0.71322 0.77373 0.66937 0.56745 0.52899 0.95812 0.00095644 0.00095644 0.00095644
25 24 10856.2 0.62557 0.66267 1.01335 0.79272 0.71389 0.77391 0.66966 0.56675 0.52595 0.95704 0.00095446 0.00095446 0.00095446
26 25 11307.8 0.6228 0.66036 1.01338 0.79825 0.7089 0.77352 0.66961 0.56559 0.52478 0.95629 0.00095248 0.00095248 0.00095248
27 26 11759 0.61943 0.64768 1.00938 0.80146 0.70794 0.77467 0.67145 0.56379 0.52484 0.95511 0.0009505 0.0009505 0.0009505
28 27 12209.9 0.61572 0.64537 1.00894 0.80189 0.70873 0.77538 0.67173 0.56254 0.52454 0.95405 0.00094852 0.00094852 0.00094852
29 28 12661.1 0.6106 0.63838 1.00674 0.8059 0.69691 0.77772 0.67449 0.56148 0.52311 0.95344 0.00094654 0.00094654 0.00094654
30 29 13112.4 0.61079 0.63358 1.00562 0.81364 0.6993 0.77911 0.67521 0.56085 0.52151 0.95275 0.00094456 0.00094456 0.00094456
31 30 13563.4 0.60736 0.62771 1.0039 0.8167 0.70162 0.78063 0.67704 0.56021 0.51914 0.95196 0.00094258 0.00094258 0.00094258
32 31 14014.7 0.60259 0.61985 0.9999 0.82663 0.69761 0.78214 0.67842 0.55884 0.51673 0.95085 0.0009406 0.0009406 0.0009406
33 32 14465.7 0.60076 0.61614 0.9998 0.81385 0.70055 0.78048 0.67785 0.55743 0.51473 0.94952 0.00093862 0.00093862 0.00093862
34 33 14916.9 0.59559 0.61001 0.9963 0.7904 0.72103 0.78147 0.67937 0.55604 0.5144 0.94898 0.00093664 0.00093664 0.00093664
35 34 15368.1 0.59677 0.60432 0.99745 0.79309 0.72155 0.78511 0.681 0.55421 0.51439 0.94781 0.00093466 0.00093466 0.00093466
36 35 15819 0.59436 0.59981 0.9958 0.78066 0.72799 0.78594 0.68171 0.55363 0.51403 0.94769 0.00093268 0.00093268 0.00093268
37 36 16269.4 0.58839 0.5893 0.98983 0.83254 0.6988 0.78782 0.68435 0.55268 0.51254 0.94686 0.0009307 0.0009307 0.0009307
38 37 16720.5 0.59039 0.59206 0.99453 0.83285 0.69858 0.79044 0.68656 0.55115 0.51143 0.94562 0.00092872 0.00092872 0.00092872
39 38 17172.3 0.58499 0.58754 0.99226 0.83092 0.7069 0.79024 0.68596 0.54963 0.50939 0.94472 0.00092674 0.00092674 0.00092674
40 39 17622.8 0.58285 0.58114 0.98975 0.83099 0.70834 0.78883 0.6843 0.54931 0.50774 0.94484 0.00092476 0.00092476 0.00092476
41 40 18074 0.58215 0.57689 0.98926 0.8316 0.70731 0.78625 0.68251 0.54983 0.50725 0.9454 0.00092278 0.00092278 0.00092278
42 41 18525 0.5798 0.57245 0.98881 0.82733 0.71305 0.78675 0.6837 0.5492 0.50711 0.94548 0.0009208 0.0009208 0.0009208
43 42 18975.5 0.57903 0.56826 0.98735 0.83222 0.71435 0.78699 0.68425 0.54926 0.50586 0.94598 0.00091882 0.00091882 0.00091882
44 43 19427 0.57443 0.56251 0.98391 0.83123 0.71496 0.78895 0.68475 0.54733 0.50456 0.9448 0.00091684 0.00091684 0.00091684
45 44 19878.7 0.57384 0.55961 0.98381 0.83089 0.71451 0.78977 0.68517 0.54677 0.50361 0.94482 0.00091486 0.00091486 0.00091486
46 45 20329.4 0.5729 0.55585 0.98206 0.82506 0.71059 0.79006 0.6856 0.54644 0.50347 0.94517 0.00091288 0.00091288 0.00091288
47 46 20780.7 0.5705 0.5519 0.98273 0.82193 0.71212 0.7893 0.68622 0.54526 0.50296 0.94488 0.0009109 0.0009109 0.0009109
48 47 21231.5 0.57039 0.54936 0.98328 0.81485 0.71467 0.78967 0.68573 0.54498 0.50265 0.94469 0.00090892 0.00090892 0.00090892
49 48 21682.7 0.56927 0.54922 0.98108 0.80506 0.72397 0.79063 0.68688 0.54456 0.5021 0.94483 0.00090694 0.00090694 0.00090694
50 49 22134.1 0.56478 0.54181 0.97904 0.79302 0.73117 0.78946 0.68703 0.54406 0.50194 0.94468 0.00090496 0.00090496 0.00090496
51 50 22585.7 0.56338 0.53745 0.97706 0.77517 0.73775 0.78941 0.68732 0.54371 0.50229 0.9444 0.00090298 0.00090298 0.00090298
52 51 23036.7 0.56128 0.53292 0.97478 0.80155 0.71624 0.78828 0.68467 0.54271 0.5022 0.94369 0.000901 0.000901 0.000901
53 52 23487.8 0.56123 0.53205 0.97435 0.80061 0.71755 0.78773 0.68522 0.5422 0.50133 0.94338 0.00089902 0.00089902 0.00089902
54 53 23938.2 0.55887 0.52587 0.97366 0.79307 0.71911 0.78783 0.68579 0.54101 0.50179 0.94267 0.00089704 0.00089704 0.00089704
55 54 24389.2 0.5569 0.52658 0.97362 0.77412 0.72758 0.7872 0.68469 0.54015 0.5029 0.9424 0.00089506 0.00089506 0.00089506
56 55 24840.5 0.55622 0.52079 0.97329 0.76262 0.74176 0.7893 0.68829 0.53902 0.50352 0.94193 0.00089308 0.00089308 0.00089308
57 56 25291.9 0.55542 0.52175 0.97347 0.75164 0.75496 0.78918 0.68887 0.53851 0.50406 0.94191 0.0008911 0.0008911 0.0008911
58 57 25742.9 0.5535 0.51809 0.97131 0.74686 0.75478 0.78671 0.68904 0.53857 0.50484 0.94218 0.00088912 0.00088912 0.00088912
59 58 26194.2 0.55208 0.51515 0.97343 0.75846 0.74752 0.78558 0.68812 0.53858 0.5059 0.94267 0.00088714 0.00088714 0.00088714
60 59 26645.4 0.55066 0.51327 0.97164 0.77666 0.72557 0.78524 0.68861 0.53804 0.50633 0.9427 0.00088516 0.00088516 0.00088516
61 60 27096.8 0.54974 0.51001 0.9694 0.7264 0.77398 0.78355 0.68611 0.53658 0.50597 0.94206 0.00088318 0.00088318 0.00088318
62 61 27548.1 0.54559 0.5052 0.96733 0.75503 0.75642 0.78355 0.68604 0.53534 0.50628 0.94114 0.0008812 0.0008812 0.0008812
63 62 27999.4 0.54474 0.50356 0.96697 0.76073 0.75125 0.78297 0.68622 0.53525 0.50683 0.94149 0.00087922 0.00087922 0.00087922
64 63 28450.5 0.54489 0.50109 0.96692 0.76253 0.74972 0.78137 0.68431 0.53442 0.50701 0.94117 0.00087724 0.00087724 0.00087724
65 64 28901.9 0.54279 0.49894 0.96659 0.7633 0.74753 0.78198 0.68422 0.534 0.50808 0.94112 0.00087526 0.00087526 0.00087526
66 65 29353.6 0.5446 0.49924 0.96593 0.76671 0.75031 0.78116 0.68332 0.53472 0.50707 0.94103 0.00087328 0.00087328 0.00087328
67 66 29804.2 0.54173 0.49441 0.96263 0.76454 0.75281 0.78041 0.68392 0.53482 0.5069 0.94137 0.0008713 0.0008713 0.0008713
68 67 30255.2 0.53911 0.49062 0.96278 0.76411 0.75269 0.78029 0.68218 0.53491 0.50599 0.94152 0.00086932 0.00086932 0.00086932
69 68 30706.1 0.53856 0.48794 0.96289 0.76645 0.75188 0.78076 0.68253 0.53421 0.50596 0.94114 0.00086734 0.00086734 0.00086734
70 69 31157.4 0.53968 0.48909 0.96455 0.76773 0.74754 0.78074 0.68375 0.53362 0.50555 0.94038 0.00086536 0.00086536 0.00086536
71 70 31608.5 0.53628 0.48699 0.96154 0.77422 0.74093 0.77942 0.68391 0.53369 0.50468 0.94026 0.00086338 0.00086338 0.00086338
72 71 32060.1 0.53537 0.48481 0.96135 0.78093 0.73938 0.77988 0.68439 0.53398 0.50439 0.94033 0.0008614 0.0008614 0.0008614
73 72 32510.8 0.53519 0.48424 0.96144 0.77374 0.7459 0.78217 0.6873 0.53426 0.50416 0.94064 0.00085942 0.00085942 0.00085942
74 73 32962 0.53448 0.48211 0.96052 0.76365 0.74936 0.78153 0.68739 0.53397 0.50588 0.94086 0.00085744 0.00085744 0.00085744
75 74 33413.1 0.53141 0.47597 0.95823 0.76587 0.74857 0.7824 0.68773 0.53435 0.50584 0.94125 0.00085546 0.00085546 0.00085546
76 75 33864.1 0.53026 0.47387 0.95857 0.77682 0.7289 0.78131 0.68765 0.53429 0.50494 0.94158 0.00085348 0.00085348 0.00085348
77 76 34315 0.52914 0.4735 0.95905 0.77719 0.73672 0.78165 0.68694 0.534 0.50556 0.94164 0.0008515 0.0008515 0.0008515
78 77 34766 0.53026 0.47048 0.95843 0.7549 0.75072 0.78287 0.68702 0.53376 0.50522 0.94169 0.00084952 0.00084952 0.00084952
79 78 35216.7 0.52681 0.47115 0.95676 0.75612 0.7557 0.78295 0.68559 0.53249 0.50593 0.94083 0.00084754 0.00084754 0.00084754
80 79 35667.8 0.52582 0.46752 0.95797 0.75908 0.7579 0.781 0.68412 0.53292 0.50724 0.94153 0.00084556 0.00084556 0.00084556
81 80 36118.6 0.52694 0.46825 0.95616 0.749 0.7733 0.78172 0.68458 0.53265 0.50986 0.9416 0.00084358 0.00084358 0.00084358
82 81 36570.1 0.52658 0.46707 0.95675 0.74576 0.76459 0.78112 0.68344 0.5338 0.51163 0.94245 0.0008416 0.0008416 0.0008416
83 82 37021.7 0.52438 0.46259 0.95575 0.73804 0.78133 0.78056 0.68356 0.53479 0.51276 0.94353 0.00083962 0.00083962 0.00083962
84 83 37473.3 0.5246 0.46244 0.95754 0.74546 0.77143 0.77955 0.68195 0.535 0.51347 0.94416 0.00083764 0.00083764 0.00083764
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86 85 38376 0.52085 0.45942 0.9541 0.74132 0.78106 0.77977 0.68229 0.5361 0.51318 0.94447 0.00083368 0.00083368 0.00083368
87 86 38827.5 0.52124 0.45523 0.95424 0.73883 0.77121 0.78252 0.68506 0.53639 0.5152 0.94492 0.0008317 0.0008317 0.0008317
88 87 39279.1 0.5171 0.45303 0.95061 0.76468 0.75787 0.7794 0.68039 0.53727 0.51576 0.9456 0.00082972 0.00082972 0.00082972
89 88 39730.2 0.52005 0.45384 0.95344 0.76706 0.75256 0.77996 0.68002 0.53756 0.51479 0.94616 0.00082774 0.00082774 0.00082774
90 89 40181.8 0.51805 0.45267 0.95219 0.76351 0.7541 0.78124 0.68107 0.53802 0.51426 0.94696 0.00082576 0.00082576 0.00082576
91 90 40633.1 0.51703 0.45019 0.95272 0.73783 0.77513 0.78246 0.68086 0.53837 0.51332 0.94776 0.00082378 0.00082378 0.00082378
92 91 41084.8 0.51559 0.44851 0.95219 0.81122 0.7152 0.78376 0.6829 0.5393 0.5135 0.94856 0.0008218 0.0008218 0.0008218
93 92 41535.9 0.51465 0.44659 0.95235 0.807 0.71948 0.7853 0.68338 0.54013 0.51449 0.94903 0.00081982 0.00081982 0.00081982
94 93 41986.8 0.51449 0.44483 0.95123 0.74369 0.77954 0.78389 0.68213 0.54058 0.51453 0.94929 0.00081784 0.00081784 0.00081784
95 94 42438.8 0.51396 0.4426 0.94942 0.75636 0.77138 0.78564 0.68528 0.5398 0.51464 0.94925 0.00081586 0.00081586 0.00081586
96 95 42889.9 0.51486 0.44344 0.95128 0.75353 0.76984 0.78619 0.68502 0.53954 0.51507 0.94948 0.00081388 0.00081388 0.00081388
97 96 43341.1 0.51151 0.44189 0.94857 0.75198 0.77322 0.78581 0.68452 0.54018 0.5163 0.95016 0.0008119 0.0008119 0.0008119
98 97 43792.6 0.50917 0.43834 0.94702 0.77034 0.75353 0.78541 0.68504 0.53944 0.51656 0.95005 0.00080992 0.00080992 0.00080992
99 98 44243.9 0.51041 0.43968 0.94815 0.7627 0.76859 0.78607 0.68568 0.53983 0.51754 0.95151 0.00080794 0.00080794 0.00080794
100 99 44695.2 0.50957 0.43676 0.94735 0.75896 0.76465 0.78632 0.68583 0.53844 0.51808 0.95059 0.00080596 0.00080596 0.00080596
101 100 45146.3 0.50745 0.43537 0.94694 0.75391 0.77257 0.78599 0.68651 0.53775 0.51787 0.95023 0.00080398 0.00080398 0.00080398
102 101 45597.5 0.50812 0.43582 0.94704 0.77745 0.75832 0.78675 0.68662 0.53684 0.51773 0.95023 0.000802 0.000802 0.000802
103 102 46048.1 0.50562 0.43169 0.94502 0.78022 0.75828 0.78581 0.68707 0.53733 0.51798 0.95139 0.00080002 0.00080002 0.00080002
104 103 46499.1 0.50575 0.43067 0.94398 0.75636 0.77235 0.7825 0.68496 0.5371 0.51874 0.95146 0.00079804 0.00079804 0.00079804
105 104 46949.4 0.50372 0.42811 0.94322 0.76878 0.75628 0.78152 0.68417 0.53661 0.51882 0.95104 0.00079606 0.00079606 0.00079606
106 105 47400.5 0.50518 0.4299 0.94321 0.80925 0.7268 0.78229 0.68461 0.5364 0.5197 0.95073 0.00079408 0.00079408 0.00079408
107 106 47851.8 0.50258 0.42721 0.94219 0.77003 0.75321 0.78269 0.68599 0.5354 0.52134 0.94919 0.0007921 0.0007921 0.0007921
108 107 48302.5 0.50185 0.4262 0.94187 0.77956 0.74669 0.78216 0.68629 0.53593 0.52174 0.94943 0.00079012 0.00079012 0.00079012

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fashion_yolo/fashionpeida_exp.ipynb
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fashion_yolo/init_process.ipynb
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33
fashion_yolo/train.py
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from ultralytics import YOLO
# 1. 모델 로드 (YOLOv8m 사용)
model = YOLO('yolov8m.pt')
# 2. 학습 실행
# 위에서 생성된 yaml 파일 경로를 넣어줍니다.
# train_results = model.train(
# data="/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_custom_noface.yaml",
# epochs=500,
# imgsz=640,
# batch=-1,
# device="cuda",
# optimizer='AdamW',
# lr0=0.001,
# patience=50,
# verbose=False,
# project='fashionpedia_exp',
# name='yolov8m_fashion_noface',
# )
train_results = model.train(
data="/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_reduced.yaml",
epochs=500,
imgsz=640,
batch=-1,
device="cuda",
optimizer='AdamW',
lr0=0.001,
patience=50,
project='fashionpedia_exp',
name='yolov8m_fashion_final',
)

106
fire_smoke_detect/fire_detect/epo200_frac_0.2/args.yaml
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task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml
epochs: 200
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: fire_detect
name: epo200_frac_0.2
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.2
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.2

25
fire_smoke_detect/fire_detect/epo200_frac_0.2/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,1204.07,0.95333,0.88297,1.17171,0.58992,0.55779,0.52314,0.30558,1.48042,136.788,1.7476,0.0670152,0.00033318,0.00033318
2,2383.91,0.79944,0.57825,1.07539,0.73966,0.5336,0.6041,0.36806,1.46003,38.3215,1.63618,0.0340119,0.000663214,0.000663214
3,3566.65,0.75066,0.51917,1.0484,0.75522,0.58482,0.65317,0.41012,1.46256,44.4346,1.79015,0.00100533,0.000989948,0.000989948
4,4735.57,0.7019,0.46977,1.02273,0.75449,0.63855,0.68668,0.4302,1.44598,44.1865,1.79038,0.00098515,0.00098515,0.00098515
5,5859.2,0.66301,0.4336,1.00166,0.77177,0.62853,0.6912,0.44481,1.44808,25.727,1.82837,0.0009802,0.0009802,0.0009802
6,6968.48,0.63622,0.40996,0.98828,0.77734,0.61917,0.68599,0.44422,1.46789,18.762,1.85945,0.00097525,0.00097525,0.00097525
7,8081.49,0.61699,0.39367,0.97882,0.77549,0.62608,0.69685,0.45085,1.48469,20.1125,1.89911,0.0009703,0.0009703,0.0009703
8,9220.8,0.60255,0.38103,0.97085,0.78339,0.62475,0.69614,0.45177,1.49066,17.4359,1.9208,0.00096535,0.00096535,0.00096535
9,10376.2,0.59013,0.37139,0.96545,0.78828,0.62522,0.69971,0.45559,1.49618,15.4245,1.93214,0.0009604,0.0009604,0.0009604
10,11515.2,0.58102,0.36325,0.96117,0.79348,0.61961,0.70075,0.45759,1.51214,13.5958,1.95824,0.00095545,0.00095545,0.00095545
11,12674.1,0.57188,0.35542,0.9561,0.79279,0.61913,0.70323,0.46097,1.52172,11.9689,1.98155,0.0009505,0.0009505,0.0009505
12,13823.3,0.56364,0.34945,0.9517,0.80111,0.61334,0.70511,0.46359,1.53195,10.5718,2.00195,0.00094555,0.00094555,0.00094555
13,14995.6,0.55753,0.34407,0.94937,0.80248,0.61197,0.70717,0.46689,1.5384,9.1532,2.01451,0.0009406,0.0009406,0.0009406
14,16151.8,0.55225,0.33933,0.94648,0.79867,0.61024,0.70813,0.46864,1.54897,8.68019,2.04261,0.00093565,0.00093565,0.00093565
15,17275.9,0.54685,0.33535,0.9443,0.80185,0.60429,0.70758,0.46968,1.54495,8.10419,2.05172,0.0009307,0.0009307,0.0009307
16,18394.7,0.54267,0.33146,0.94234,0.80234,0.60287,0.70742,0.4705,1.55287,7.80516,2.07675,0.00092575,0.00092575,0.00092575
17,19541,0.53647,0.32675,0.93905,0.80107,0.60273,0.70643,0.47033,1.56068,8.17225,2.09467,0.0009208,0.0009208,0.0009208
18,20666.8,0.53343,0.3251,0.93774,0.79937,0.60026,0.70523,0.47066,1.55671,8.13241,2.0926,0.00091585,0.00091585,0.00091585
19,21805.2,0.52936,0.32165,0.9363,0.80178,0.59748,0.70382,0.47064,1.55926,8.47417,2.10418,0.0009109,0.0009109,0.0009109
20,22964,0.52662,0.31932,0.93506,0.80164,0.59668,0.70238,0.4695,1.56229,8.74841,2.10729,0.00090595,0.00090595,0.00090595
21,24118.8,0.52338,0.3163,0.93373,0.80306,0.59725,0.70186,0.46896,1.57132,9.11794,2.12589,0.000901,0.000901,0.000901
22,25255.9,0.52038,0.31379,0.93242,0.80596,0.5975,0.70275,0.4691,1.57325,9.34025,2.14093,0.00089605,0.00089605,0.00089605
23,26383.7,0.51748,0.3118,0.93098,0.8063,0.59731,0.70253,0.46937,1.56901,9.38138,2.13283,0.0008911,0.0008911,0.0008911
24,27530.6,0.51485,0.31016,0.92922,0.80466,0.5979,0.70179,0.46895,1.57327,9.19169,2.14732,0.00088615,0.00088615,0.00088615
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 1204.07 0.95333 0.88297 1.17171 0.58992 0.55779 0.52314 0.30558 1.48042 136.788 1.7476 0.0670152 0.00033318 0.00033318
3 2 2383.91 0.79944 0.57825 1.07539 0.73966 0.5336 0.6041 0.36806 1.46003 38.3215 1.63618 0.0340119 0.000663214 0.000663214
4 3 3566.65 0.75066 0.51917 1.0484 0.75522 0.58482 0.65317 0.41012 1.46256 44.4346 1.79015 0.00100533 0.000989948 0.000989948
5 4 4735.57 0.7019 0.46977 1.02273 0.75449 0.63855 0.68668 0.4302 1.44598 44.1865 1.79038 0.00098515 0.00098515 0.00098515
6 5 5859.2 0.66301 0.4336 1.00166 0.77177 0.62853 0.6912 0.44481 1.44808 25.727 1.82837 0.0009802 0.0009802 0.0009802
7 6 6968.48 0.63622 0.40996 0.98828 0.77734 0.61917 0.68599 0.44422 1.46789 18.762 1.85945 0.00097525 0.00097525 0.00097525
8 7 8081.49 0.61699 0.39367 0.97882 0.77549 0.62608 0.69685 0.45085 1.48469 20.1125 1.89911 0.0009703 0.0009703 0.0009703
9 8 9220.8 0.60255 0.38103 0.97085 0.78339 0.62475 0.69614 0.45177 1.49066 17.4359 1.9208 0.00096535 0.00096535 0.00096535
10 9 10376.2 0.59013 0.37139 0.96545 0.78828 0.62522 0.69971 0.45559 1.49618 15.4245 1.93214 0.0009604 0.0009604 0.0009604
11 10 11515.2 0.58102 0.36325 0.96117 0.79348 0.61961 0.70075 0.45759 1.51214 13.5958 1.95824 0.00095545 0.00095545 0.00095545
12 11 12674.1 0.57188 0.35542 0.9561 0.79279 0.61913 0.70323 0.46097 1.52172 11.9689 1.98155 0.0009505 0.0009505 0.0009505
13 12 13823.3 0.56364 0.34945 0.9517 0.80111 0.61334 0.70511 0.46359 1.53195 10.5718 2.00195 0.00094555 0.00094555 0.00094555
14 13 14995.6 0.55753 0.34407 0.94937 0.80248 0.61197 0.70717 0.46689 1.5384 9.1532 2.01451 0.0009406 0.0009406 0.0009406
15 14 16151.8 0.55225 0.33933 0.94648 0.79867 0.61024 0.70813 0.46864 1.54897 8.68019 2.04261 0.00093565 0.00093565 0.00093565
16 15 17275.9 0.54685 0.33535 0.9443 0.80185 0.60429 0.70758 0.46968 1.54495 8.10419 2.05172 0.0009307 0.0009307 0.0009307
17 16 18394.7 0.54267 0.33146 0.94234 0.80234 0.60287 0.70742 0.4705 1.55287 7.80516 2.07675 0.00092575 0.00092575 0.00092575
18 17 19541 0.53647 0.32675 0.93905 0.80107 0.60273 0.70643 0.47033 1.56068 8.17225 2.09467 0.0009208 0.0009208 0.0009208
19 18 20666.8 0.53343 0.3251 0.93774 0.79937 0.60026 0.70523 0.47066 1.55671 8.13241 2.0926 0.00091585 0.00091585 0.00091585
20 19 21805.2 0.52936 0.32165 0.9363 0.80178 0.59748 0.70382 0.47064 1.55926 8.47417 2.10418 0.0009109 0.0009109 0.0009109
21 20 22964 0.52662 0.31932 0.93506 0.80164 0.59668 0.70238 0.4695 1.56229 8.74841 2.10729 0.00090595 0.00090595 0.00090595
22 21 24118.8 0.52338 0.3163 0.93373 0.80306 0.59725 0.70186 0.46896 1.57132 9.11794 2.12589 0.000901 0.000901 0.000901
23 22 25255.9 0.52038 0.31379 0.93242 0.80596 0.5975 0.70275 0.4691 1.57325 9.34025 2.14093 0.00089605 0.00089605 0.00089605
24 23 26383.7 0.51748 0.3118 0.93098 0.8063 0.59731 0.70253 0.46937 1.56901 9.38138 2.13283 0.0008911 0.0008911 0.0008911
25 24 27530.6 0.51485 0.31016 0.92922 0.80466 0.5979 0.70179 0.46895 1.57327 9.19169 2.14732 0.00088615 0.00088615 0.00088615

106
fire_smoke_detect/fire_detect/epo200_frac_0.22/args.yaml
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task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml
epochs: 200
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: fire_detect
name: epo200_frac_0.22
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.2
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.22

2
fire_smoke_detect/fire_detect/epo200_frac_0.22/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,1222.25,0.95333,0.88297,1.17171,0.58992,0.55779,0.52314,0.30558,1.48042,136.788,1.7476,0.0670152,0.00033318,0.00033318
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 1222.25 0.95333 0.88297 1.17171 0.58992 0.55779 0.52314 0.30558 1.48042 136.788 1.7476 0.0670152 0.00033318 0.00033318

106
fire_smoke_detect/fire_detect/epo200_frac_0.23/args.yaml
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task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml
epochs: 200
time: null
patience: 30
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: fire_detect
name: epo200_frac_0.23
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.2
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23

49
fire_smoke_detect/fire_detect/epo200_frac_0.23/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
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1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 1231.29 0.95333 0.88297 1.17171 0.58992 0.55779 0.52314 0.30558 1.48042 136.788 1.7476 0.0670152 0.00033318 0.00033318
3 2 2305.09 0.79944 0.57825 1.07539 0.73966 0.5336 0.6041 0.36806 1.46003 38.3215 1.63618 0.0340119 0.000663214 0.000663214
4 3 3266.85 0.75066 0.51917 1.0484 0.75522 0.58482 0.65317 0.41012 1.46256 44.4346 1.79015 0.00100533 0.000989948 0.000989948
5 4 4223.68 0.7019 0.46977 1.02273 0.75449 0.63855 0.68668 0.4302 1.44598 44.1865 1.79038 0.00098515 0.00098515 0.00098515
6 5 5164.86 0.66301 0.4336 1.00166 0.77177 0.62853 0.6912 0.44481 1.44808 25.727 1.82837 0.0009802 0.0009802 0.0009802
7 6 6107.2 0.63622 0.40996 0.98828 0.77734 0.61917 0.68599 0.44422 1.46789 18.762 1.85945 0.00097525 0.00097525 0.00097525
8 7 7044.13 0.61699 0.39367 0.97882 0.77549 0.62608 0.69685 0.45085 1.48469 20.1125 1.89911 0.0009703 0.0009703 0.0009703
9 8 7986.66 0.60255 0.38103 0.97085 0.78339 0.62475 0.69614 0.45177 1.49066 17.4359 1.9208 0.00096535 0.00096535 0.00096535
10 9 8921.81 0.59013 0.37139 0.96545 0.78828 0.62522 0.69971 0.45559 1.49618 15.4245 1.93214 0.0009604 0.0009604 0.0009604
11 10 9846.06 0.58102 0.36325 0.96117 0.79348 0.61961 0.70075 0.45759 1.51214 13.5958 1.95824 0.00095545 0.00095545 0.00095545
12 11 10771.6 0.57188 0.35542 0.9561 0.79279 0.61913 0.70323 0.46097 1.52172 11.9689 1.98155 0.0009505 0.0009505 0.0009505
13 12 11690.6 0.56364 0.34945 0.9517 0.80111 0.61334 0.70511 0.46359 1.53195 10.5718 2.00195 0.00094555 0.00094555 0.00094555
14 13 12610.4 0.55753 0.34407 0.94937 0.80248 0.61197 0.70717 0.46689 1.5384 9.1532 2.01451 0.0009406 0.0009406 0.0009406
15 14 13530.7 0.55225 0.33933 0.94648 0.79867 0.61024 0.70813 0.46864 1.54897 8.68019 2.04261 0.00093565 0.00093565 0.00093565
16 15 14450.7 0.54685 0.33535 0.9443 0.80185 0.60429 0.70758 0.46968 1.54495 8.10419 2.05172 0.0009307 0.0009307 0.0009307
17 16 15368.8 0.54267 0.33146 0.94234 0.80234 0.60287 0.70742 0.4705 1.55287 7.80516 2.07675 0.00092575 0.00092575 0.00092575
18 17 16304.6 0.53647 0.32675 0.93905 0.80107 0.60273 0.70643 0.47033 1.56068 8.17225 2.09467 0.0009208 0.0009208 0.0009208
19 18 17249.5 0.53343 0.3251 0.93774 0.79937 0.60026 0.70523 0.47066 1.55671 8.13241 2.0926 0.00091585 0.00091585 0.00091585
20 19 18197.8 0.52936 0.32165 0.9363 0.80178 0.59748 0.70382 0.47064 1.55926 8.47417 2.10418 0.0009109 0.0009109 0.0009109
21 20 19143.1 0.52662 0.31932 0.93506 0.80164 0.59668 0.70238 0.4695 1.56229 8.74841 2.10729 0.00090595 0.00090595 0.00090595
22 21 20080.6 0.52338 0.3163 0.93373 0.80306 0.59725 0.70186 0.46896 1.57132 9.11794 2.12589 0.000901 0.000901 0.000901
23 22 21026.9 0.52038 0.31379 0.93242 0.80596 0.5975 0.70275 0.4691 1.57325 9.34025 2.14093 0.00089605 0.00089605 0.00089605
24 23 21973.4 0.51748 0.3118 0.93098 0.8063 0.59731 0.70253 0.46937 1.56901 9.38138 2.13283 0.0008911 0.0008911 0.0008911
25 24 22909.7 0.51485 0.31016 0.92922 0.80466 0.5979 0.70179 0.46895 1.57327 9.19169 2.14732 0.00088615 0.00088615 0.00088615
26 25 23850.8 inf 0.30745 0.92818 0.80276 0.59834 0.70088 0.46777 1.57743 9.46807 2.16309 0.0008812 0.0008812 0.0008812
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28 27 25726.8 0.5076 0.30424 0.92655 0.80403 0.59709 0.7002 0.46536 1.57713 10.1806 2.16274 0.0008713 0.0008713 0.0008713
29 28 26661.7 0.50585 0.30305 0.92576 0.80228 0.5971 0.6998 0.46502 1.57733 10.1693 2.1652 0.00086635 0.00086635 0.00086635
30 29 27599.4 0.50306 0.30056 0.92434 0.80314 0.59518 0.69922 0.46453 1.58016 9.98943 2.17888 0.0008614 0.0008614 0.0008614
31 30 28538.5 0.50155 0.29911 0.92367 0.8028 0.59304 0.69757 0.46292 1.58332 10.546 2.18906 0.00085645 0.00085645 0.00085645
32 31 29477.3 0.49893 0.29753 0.9224 0.80206 0.59255 0.69616 0.46183 1.58237 11.1724 2.1932 0.0008515 0.0008515 0.0008515
33 32 30419.6 0.49697 0.29575 0.92136 0.80252 0.59236 0.69581 0.46209 1.58474 11.3598 2.19785 0.00084655 0.00084655 0.00084655
34 33 31345.4 0.49527 0.2948 0.92082 0.80121 0.59174 0.6951 0.46091 1.58227 12.4389 2.19247 0.0008416 0.0008416 0.0008416
35 34 32280.3 0.49344 0.29327 0.91958 0.8004 0.59011 0.6945 0.45992 1.58068 12.9417 2.1865 0.00083665 0.00083665 0.00083665
36 35 33212.5 0.49199 0.29225 0.91942 0.79787 0.5911 0.69395 0.45908 1.57832 13.358 2.1876 0.0008317 0.0008317 0.0008317
37 36 34147.6 0.49082 0.2914 0.91864 0.79629 0.59203 0.69372 0.45872 1.58015 13.725 2.19326 0.00082675 0.00082675 0.00082675
38 37 35079.1 0.48962 0.29024 0.9183 0.79505 0.59202 0.69331 0.45841 1.58044 14.7538 2.20086 0.0008218 0.0008218 0.0008218
39 38 36002.2 0.48822 0.28901 0.9174 0.7924 0.59201 0.69178 0.45807 1.5832 14.9679 2.20654 0.00081685 0.00081685 0.00081685
40 39 36936.4 0.48609 0.28799 0.91666 0.78931 0.59349 0.69133 0.45783 1.58224 15.523 2.2005 0.0008119 0.0008119 0.0008119
41 40 37873.4 0.48492 0.28646 0.91595 0.78576 0.59477 0.69122 0.45733 1.57714 15.8603 2.18981 0.00080695 0.00080695 0.00080695
42 41 38809.6 0.48358 0.28569 0.91544 0.78608 0.594 0.69158 0.45675 1.58096 16.1674 2.1928 0.000802 0.000802 0.000802
43 42 39745.5 0.48262 0.28443 0.91611 0.78173 0.59728 0.69192 0.45633 1.58189 16.9209 2.20177 0.00079705 0.00079705 0.00079705
44 43 40675 0.48087 0.28369 0.91546 0.78293 0.59837 0.69231 0.45555 1.58687 17.469 2.2154 0.0007921 0.0007921 0.0007921
45 44 41612.1 0.47984 0.28248 0.91398 0.78191 0.59908 0.69255 0.45503 1.58852 16.9581 2.22694 0.00078715 0.00078715 0.00078715
46 45 42561.8 inf 0.28182 0.91365 0.78538 0.59734 0.69201 0.45388 1.59065 17.8763 2.23381 0.0007822 0.0007822 0.0007822
47 46 43500.5 0.47734 0.28076 0.91301 0.78908 0.59446 0.69184 0.454 1.59332 17.9766 2.24166 0.00077725 0.00077725 0.00077725
48 47 44444.4 0.47562 0.28004 0.9127 0.78747 0.59572 0.69197 0.4531 1.59389 18.2731 2.24922 0.0007723 0.0007723 0.0007723
49 48 45385.8 0.47473 0.27879 0.91198 0.78282 0.59854 0.69222 0.45333 1.59942 18.0307 2.25994 0.00076735 0.00076735 0.00076735

106
fire_smoke_detect/fire_detect/test/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml
epochs: 3
time: null
patience: 2
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: fire_detect
name: test
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 0.2
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/fire_smoke_detect/fire_detect/test

4
fire_smoke_detect/fire_detect/test/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,1199.61,0.95333,0.88297,1.17171,0.58992,0.55779,0.52314,0.30558,1.48042,136.788,1.7476,0.0670152,0.00033318,0.00033318
2,2232.52,0.76985,0.54965,1.05934,0.72812,0.57948,0.63698,0.39352,1.45385,49.7197,1.71483,0.0337953,0.000446564,0.000446564
3,3210.1,0.68091,0.45689,1.01277,0.77079,0.60657,0.6896,0.43966,1.37136,36.4624,1.57494,0.00035533,0.000339948,0.000339948
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 1199.61 0.95333 0.88297 1.17171 0.58992 0.55779 0.52314 0.30558 1.48042 136.788 1.7476 0.0670152 0.00033318 0.00033318
3 2 2232.52 0.76985 0.54965 1.05934 0.72812 0.57948 0.63698 0.39352 1.45385 49.7197 1.71483 0.0337953 0.000446564 0.000446564
4 3 3210.1 0.68091 0.45689 1.01277 0.77079 0.60657 0.6896 0.43966 1.37136 36.4624 1.57494 0.00035533 0.000339948 0.000339948

820
fire_smoke_detect/fire_exp.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a7676704",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3e94066a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "99b0442c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New https://pypi.org/project/ultralytics/8.3.227 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.8.0+cu129 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=200, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=0.2, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=epo200_frac_0.23, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=30, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=fire_detect, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=2\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
" 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n",
" 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
" 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n",
" 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
" 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
" 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n",
" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
" 22 [15, 18, 21] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] \n",
"Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs\n",
"\n",
"Transferred 319/355 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 708.3±281.4 MB/s, size: 334.0 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/fire_dataset/Training/labels/FL/ENB/0174/JPG.cache... 305424 images, 1715 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 305424/305424 572.9Mit/s 0.0s\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 0.15G reserved, 0.05G allocated, 31.13G free\n",
" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
" 3011238 8.195 3.450 35.09 227.5 (1, 3, 640, 640) list\n",
" 3011238 16.39 3.991 4.973 24.53 (2, 3, 640, 640) list\n",
" 3011238 32.78 4.261 5.241 24.53 (4, 3, 640, 640) list\n",
" 3011238 65.56 4.970 5.51 30.21 (8, 3, 640, 640) list\n",
" 3011238 131.1 6.117 7.798 40.44 (16, 3, 640, 640) list\n",
" 3011238 262.2 4.855 15.02 54 (32, 3, 640, 640) list\n",
" 3011238 524.5 10.557 30.83 104.5 (64, 3, 640, 640) list\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 141 for CUDA:0 19.15G/31.33G (61%) ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 704.0±204.4 MB/s, size: 445.0 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/fire_dataset/Training/labels/FL/ENB/0174/JPG.cache... 305424 images, 1715 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 305424/305424 593.6Mit/s 0.0s\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 825.4±75.3 MB/s, size: 500.0 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/aihub_car/fire_dataset/Validation/labels/FL/ENB/0953/JPG.cache... 190800 images, 38386 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 190800/190800 255.3Mit/s 0.0s\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00001.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00002.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00003.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00004.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00005.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00006.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00007.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00008.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00009.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00010.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00011.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00012.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00013.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00014.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00015.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00016.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00017.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00018.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00019.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00020.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00021.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00022.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00023.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00024.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00025.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00026.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00027.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00028.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00029.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00030.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00031.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00032.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00033.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00034.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00035.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00036.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00037.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00038.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00039.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00040.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00041.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00042.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00043.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00044.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00045.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00046.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00047.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00048.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00049.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00050.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00051.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00052.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00053.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00054.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00055.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00056.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00057.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00058.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00059.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00060.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00061.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00062.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00063.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00064.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00065.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00066.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00067.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00068.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00069.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00070.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00071.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00072.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00073.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00074.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00075.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00076.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00077.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00078.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00079.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00080.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00081.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00082.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00083.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00084.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00085.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00086.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00087.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00088.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00089.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00090.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00091.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00092.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00093.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00094.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00095.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00096.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00097.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00098.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00099.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00100.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00101.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00102.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00103.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00104.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00105.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00106.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00107.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00108.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00109.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00110.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00111.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00112.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00113.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00114.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00115.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00116.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00117.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00118.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00119.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00120.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00121.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00122.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00123.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00124.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00125.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00126.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00127.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00128.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00129.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00130.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00131.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00132.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00133.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00134.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00135.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00136.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00137.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00138.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00139.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00140.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00141.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00142.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00143.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00144.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00145.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00146.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00147.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00148.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00149.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00150.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00151.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00152.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00153.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00154.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00155.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00156.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00157.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00158.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00159.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00160.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00161.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00162.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00163.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00164.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00165.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00166.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00167.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00168.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00169.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00170.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00171.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00172.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00173.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00174.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00175.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00176.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00177.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00178.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00179.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00180.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00181.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00182.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00183.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00184.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00185.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00186.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00187.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00188.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00189.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00190.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00191.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00192.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00193.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00194.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00195.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00196.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00197.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00198.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00199.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00200.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00201.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00202.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00203.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00204.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00205.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00206.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00207.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00208.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00209.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00210.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00211.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00212.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00213.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00214.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00215.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00216.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00217.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00218.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00219.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00220.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00221.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00222.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00223.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00224.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00225.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00226.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00227.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00228.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00229.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00230.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00231.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00232.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00233.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00234.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00235.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00236.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00237.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00238.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00239.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00240.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00241.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00242.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00243.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00244.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00245.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00246.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00247.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00248.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00249.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00250.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00251.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00252.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00253.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00254.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00255.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00256.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00257.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00258.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00259.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00260.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00261.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00262.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00263.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00264.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00265.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00266.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00267.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00268.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00285.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00287.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00288.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00289.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00290.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00291.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00292.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00293.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00295.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00296.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00297.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00300.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00302.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00304.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00305.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00306.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00307.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00308.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00309.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00317.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00318.jpg: 1 duplicate labels removed\n",
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"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00323.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00324.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00325.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00326.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00327.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00328.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00329.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00330.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00331.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00332.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00333.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00334.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00335.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00336.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00337.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00338.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00339.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00340.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00341.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00342.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00343.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00344.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00345.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00346.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00347.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00348.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00349.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00350.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00351.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00352.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00353.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00354.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00355.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00356.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00357.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00358.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00359.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mval: \u001b[0m/home/cuuva/aihub_car/fire_dataset/Validation/images/FL/MS/2317/JPG/2317_FL_MS_00360.jpg: 1 duplicate labels removed\n",
"Plotting labels to /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0011015625000000001), 63 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1m/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23\u001b[0m\n",
"Starting training for 200 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 1/200 18.7G 0.9533 0.883 1.172 60 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:03<0.2s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.0it/s 2:47<0.2s\n",
" all 190800 194206 0.59 0.558 0.523 0.306\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 2/200 18.7G 0.7994 0.5782 1.075 66 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:49<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.3it/s 2:39<0.2s\n",
" all 190800 194206 0.74 0.534 0.604 0.368\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 3/200 18.7G 0.7507 0.5192 1.048 60 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:48<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.3it/s 2:36<0.2s\n",
" all 190800 194206 0.755 0.585 0.653 0.41\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 4/200 18.7G 0.7019 0.4698 1.023 43 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:47<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.4it/s 2:35<0.2s\n",
" all 190800 194206 0.754 0.639 0.687 0.43\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 5/200 18.7G 0.663 0.4336 1.002 70 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:45<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.4it/s 2:33<0.2s\n",
" all 190800 194206 0.772 0.629 0.691 0.445\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 6/200 18.7G 0.6362 0.41 0.9883 53 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:48<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.4it/s 2:33<0.2s\n",
" all 190800 194206 0.777 0.619 0.686 0.444\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 7/200 18.7G 0.617 0.3937 0.9788 60 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:46<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:31<0.2s\n",
" all 190800 194206 0.775 0.626 0.697 0.451\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 8/200 18.7G 0.6026 0.381 0.9708 62 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:51<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.783 0.625 0.696 0.452\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 9/200 18.7G 0.5901 0.3714 0.9654 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:51<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.788 0.625 0.7 0.456\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 10/200 18.7G 0.581 0.3632 0.9612 57 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:47<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.793 0.62 0.701 0.458\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 11/200 18.7G 0.5719 0.3554 0.9561 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:50<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.793 0.619 0.703 0.461\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 12/200 18.7G 0.5636 0.3495 0.9517 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:45<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.801 0.613 0.705 0.464\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 13/200 18.7G 0.5575 0.3441 0.9494 66 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:46<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:26<0.3s\n",
" all 190800 194206 0.802 0.612 0.707 0.467\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 14/200 18.7G 0.5523 0.3393 0.9465 57 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:50<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:27<0.2s\n",
" all 190800 194206 0.799 0.61 0.708 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 15/200 18.7G 0.5468 0.3353 0.9443 73 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:48<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:26<0.2s\n",
" all 190800 194206 0.802 0.604 0.708 0.47\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 16/200 18.7G 0.5427 0.3315 0.9423 69 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:48<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:26<0.2s\n",
" all 190800 194206 0.802 0.603 0.707 0.471\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 17/200 18.7G 0.5365 0.3268 0.939 66 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:58<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.4it/s 2:35<0.2s\n",
" all 190800 194206 0.801 0.603 0.706 0.47\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 18/200 18.7G 0.5334 0.3251 0.9377 65 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:08<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.799 0.6 0.705 0.471\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 19/200 18.7G 0.5294 0.3216 0.9363 54 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:10<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.4it/s 2:34<0.3s\n",
" all 190800 194206 0.802 0.597 0.704 0.471\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 20/200 18.7G 0.5266 0.3193 0.9351 55 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:10<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.802 0.597 0.702 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 21/200 18.7G 0.5234 0.3163 0.9337 55 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:05<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:27<0.2s\n",
" all 190800 194206 0.803 0.597 0.702 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 22/200 18.7G 0.5204 0.3138 0.9324 67 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:11<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.3s\n",
" all 190800 194206 0.806 0.598 0.703 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 23/200 18.7G 0.5175 0.3118 0.931 68 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:14<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.806 0.597 0.703 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 24/200 18.7G 0.5149 0.3102 0.9292 56 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:05<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.3s\n",
" all 190800 194206 0.805 0.598 0.702 0.469\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 25/200 18.7G inf 0.3075 0.9282 54 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:10<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.803 0.598 0.701 0.468\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 26/200 18.7G inf 0.3061 0.9277 63 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:08<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.3s\n",
" all 190800 194206 0.803 0.598 0.701 0.467\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 27/200 18.7G 0.5076 0.3042 0.9265 46 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:03<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.804 0.597 0.7 0.465\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 28/200 18.7G 0.5059 0.3031 0.9258 71 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:02<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.802 0.597 0.7 0.465\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 29/200 18.7G 0.5031 0.3006 0.9243 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:06<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.803 0.595 0.699 0.465\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 30/200 18.7G 0.5015 0.2991 0.9237 63 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:07<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:27<0.2s\n",
" all 190800 194206 0.803 0.593 0.698 0.463\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 31/200 18.7G 0.4989 0.2975 0.9224 66 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:04<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:29<0.2s\n",
" all 190800 194206 0.802 0.593 0.696 0.462\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 32/200 18.7G 0.497 0.2958 0.9214 65 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:09<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:27<0.2s\n",
" all 190800 194206 0.803 0.592 0.696 0.462\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 33/200 18.7G 0.4953 0.2948 0.9208 42 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:55<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:27<0.2s\n",
" all 190800 194206 0.801 0.592 0.695 0.461\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 34/200 18.7G 0.4934 0.2933 0.9196 60 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:01<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:29<0.2s\n",
" all 190800 194206 0.8 0.59 0.694 0.46\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 35/200 18.7G 0.492 0.2922 0.9194 54 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:00<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.798 0.591 0.694 0.459\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 36/200 18.7G 0.4908 0.2914 0.9186 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:04<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.796 0.592 0.694 0.459\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 37/200 18.7G 0.4896 0.2902 0.9183 51 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:57<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.3s\n",
" all 190800 194206 0.795 0.592 0.693 0.458\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 38/200 18.7G 0.4882 0.289 0.9174 64 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:53<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.792 0.592 0.692 0.458\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 39/200 18.7G 0.4861 0.288 0.9167 50 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:58<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.789 0.593 0.691 0.458\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 40/200 18.7G 0.4849 0.2865 0.916 70 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:03<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.786 0.595 0.691 0.457\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 41/200 18.7G 0.4836 0.2857 0.9154 59 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:05<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:29<0.2s\n",
" all 190800 194206 0.786 0.594 0.692 0.457\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 42/200 18.7G 0.4826 0.2844 0.9161 81 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:59<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.3s\n",
" all 190800 194206 0.782 0.597 0.692 0.456\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 43/200 18.7G 0.4809 0.2837 0.9155 58 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 12:56<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.783 0.598 0.692 0.456\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 44/200 18.7G 0.4798 0.2825 0.914 62 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:04<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:29<0.2s\n",
" all 190800 194206 0.782 0.599 0.693 0.455\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 45/200 18.7G inf 0.2818 0.9137 70 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:16<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:31<0.2s\n",
" all 190800 194206 0.785 0.597 0.692 0.454\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 46/200 18.7G 0.4773 0.2808 0.913 70 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:05<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:29<0.2s\n",
" all 190800 194206 0.789 0.594 0.692 0.454\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 47/200 18.7G 0.4756 0.28 0.9127 57 640: 100% ━━━━━━━━━━━━ 2167/2167 2.7it/s 13:10<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.6it/s 2:28<0.2s\n",
" all 190800 194206 0.787 0.596 0.692 0.453\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 48/200 18.7G 0.4747 0.2788 0.912 62 640: 100% ━━━━━━━━━━━━ 2167/2167 2.8it/s 13:07<0.7s\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.5it/s 2:30<0.2s\n",
" all 190800 194206 0.783 0.599 0.692 0.453\n",
"\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 30 epochs. Best results observed at epoch 18, best model saved as best.pt.\n",
"To update EarlyStopping(patience=30) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
"\n",
"48 epochs completed in 12.607 hours.\n",
"Optimizer stripped from /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/last.pt, 6.2MB\n",
"Optimizer stripped from /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.pt, 6.2MB\n",
"\n",
"Validating /home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.pt...\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.8.0+cu129 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"Model summary (fused): 72 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 677/677 4.0it/s 2:50<0.3s\n",
" all 190800 194206 0.8 0.601 0.705 0.471\n",
" flame 72076 75235 0.819 0.759 0.837 0.605\n",
" smoke 116127 118971 0.781 0.443 0.574 0.336\n",
"Speed: 0.0ms preprocess, 0.2ms inference, 0.0ms loss, 0.2ms postprocess per image\n",
"Results saved to \u001b[1m/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23\u001b[0m\n"
]
}
],
"source": [
"# Load a pretrained YOLO11n model\n",
"model = YOLO(\"yolov8n.pt\")\n",
"\n",
"train_results = model.train(\n",
" data=\"/home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml\",\n",
" epochs=200,\n",
" imgsz=640,\n",
" batch=-1,\n",
" device=\"cuda\",\n",
" optimizer = 'AdamW',\n",
" lr0 = 0.001,\n",
" patience = 30,\n",
" project = 'fire_detect',\n",
" name = 'epo200_frac_0.2',\n",
" fraction = 0.2\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "88d6a47e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.8.0+cu129 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model summary (fused): 72 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 6, 8400) (5.9 MB)\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 2.9s, saved as '/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.onnx' (11.7 MB)\n",
"\n",
"Export complete (3.0s)\n",
"Results saved to \u001b[1m/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights\u001b[0m\n",
"Predict: yolo predict task=detect model=/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.onnx imgsz=640 \n",
"Validate: yolo val task=detect model=/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.onnx imgsz=640 data=/home/cuuva/experiment/fire_smoke_detect/fire_smoke_detect.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"data": {
"text/plain": [
"'/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.onnx'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = YOLO(\"/home/cuuva/experiment/fire_smoke_detect/fire_detect/epo200_frac_0.23/weights/best.pt\")\n",
"model.export(format=\"onnx\", imgsz=640, device=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ad3f495",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

10
fire_smoke_detect/fire_smoke_detect.yaml
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# 데이터셋 경로
train: /home/cuuva/aihub_car/fire_dataset/Training/images/
val: /home/cuuva/aihub_car/fire_dataset/Validation/images/
# 클래스 수
nc: 2
# 클래스 이름
names: ['flame', 'smoke']

106
swoon_detect/sw_detect/final_100epoch/args.yaml
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task: detect
mode: train
model: yolov8n.pt
data: /home/cuuva/experiment/swoon_detect/swoon_detect.yaml
epochs: 300
time: null
patience: 50
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: sw_detect
name: final_100epoch
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch

132
swoon_detect/sw_detect/final_100epoch/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
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59,812.447,0.51175,0.3556,0.84435,0.91532,0.87955,0.89698,0.66439,0.78617,0.44624,1.08033,0.0008086,0.0008086,0.0008086
60,822.502,0.52929,0.35839,0.8547,0.91753,0.88788,0.90612,0.67992,0.7899,0.43338,1.04613,0.0008053,0.0008053,0.0008053
61,835.085,0.53648,0.36334,0.85282,0.94742,0.88427,0.92062,0.70481,0.72008,0.4443,0.96516,0.000802,0.000802,0.000802
62,846.861,0.51907,0.35473,0.84629,0.92732,0.87703,0.89766,0.68322,0.70787,0.47261,1.01025,0.0007987,0.0007987,0.0007987
63,858.564,0.51051,0.3523,0.85127,0.92886,0.88427,0.90168,0.70173,0.69482,0.46427,0.97892,0.0007954,0.0007954,0.0007954
64,870.064,0.52071,0.3634,0.85274,0.92049,0.8698,0.88206,0.68041,0.71646,0.48517,1.00591,0.0007921,0.0007921,0.0007921
65,882.799,0.50793,0.34813,0.85074,0.9073,0.88065,0.90013,0.69868,0.70316,0.45048,0.98568,0.0007888,0.0007888,0.0007888
66,896.029,0.51142,0.34745,0.8548,0.90485,0.85353,0.89757,0.69137,0.73512,0.42862,1.05296,0.0007855,0.0007855,0.0007855
67,907.816,0.51635,0.34536,0.85241,0.92261,0.88388,0.90844,0.7153,0.67732,0.41922,1.00051,0.0007822,0.0007822,0.0007822
68,918.958,0.50808,0.35686,0.84852,0.94178,0.86257,0.90453,0.66669,0.76668,0.43723,1.07557,0.0007789,0.0007789,0.0007789
69,931.244,0.4998,0.33677,0.84352,0.92619,0.88496,0.90317,0.68321,0.75844,0.42174,1.04478,0.0007756,0.0007756,0.0007756
70,942.872,0.52196,0.35231,0.8499,0.92503,0.87021,0.90893,0.69302,0.72319,0.45712,1.01505,0.0007723,0.0007723,0.0007723
71,955.449,0.50125,0.33892,0.84865,0.91341,0.87752,0.90688,0.69514,0.723,0.41792,1.03109,0.000769,0.000769,0.000769
72,968.156,0.49979,0.34101,0.84551,0.93439,0.89331,0.9212,0.72195,0.69226,0.40073,0.97614,0.0007657,0.0007657,0.0007657
73,980.354,0.51371,0.34288,0.84347,0.93253,0.87475,0.89787,0.69665,0.71931,0.41821,0.99889,0.0007624,0.0007624,0.0007624
74,991.234,0.50302,0.33922,0.84588,0.90984,0.87342,0.90602,0.69639,0.7558,0.42219,1.05959,0.0007591,0.0007591,0.0007591
75,1004.05,0.49584,0.34472,0.84401,0.92344,0.86438,0.8959,0.68855,0.72366,0.43524,1.02242,0.0007558,0.0007558,0.0007558
76,1014.82,0.49028,0.33776,0.84343,0.92713,0.89725,0.91799,0.70346,0.7291,0.41195,1.01147,0.0007525,0.0007525,0.0007525
77,1027.12,0.49405,0.32601,0.84625,0.92347,0.89467,0.90552,0.69395,0.72762,0.41947,1.04666,0.0007492,0.0007492,0.0007492
78,1039.23,0.49736,0.33638,0.84642,0.92874,0.87703,0.91351,0.69864,0.70537,0.42223,1.02199,0.0007459,0.0007459,0.0007459
79,1051,0.49819,0.34083,0.84315,0.89828,0.88969,0.91238,0.69457,0.72477,0.42391,1.02981,0.0007426,0.0007426,0.0007426
80,1066.14,0.51095,0.3441,0.85009,0.87679,0.87703,0.87828,0.65401,0.76728,0.49605,1.10681,0.0007393,0.0007393,0.0007393
81,1079.16,0.50392,0.3406,0.84965,0.91518,0.87161,0.91496,0.74287,0.65844,0.3773,0.98431,0.000736,0.000736,0.000736
82,1090.51,0.50999,0.33647,0.84333,0.92449,0.88065,0.91303,0.72863,0.64829,0.40398,0.97032,0.0007327,0.0007327,0.0007327
83,1103.58,0.48732,0.33154,0.84668,0.90863,0.88114,0.89377,0.69624,0.71363,0.42001,1.02309,0.0007294,0.0007294,0.0007294
84,1115,0.5065,0.33619,0.84501,0.9053,0.90778,0.91101,0.69981,0.71369,0.39276,1.01418,0.0007261,0.0007261,0.0007261
85,1128.82,0.48695,0.32756,0.83675,0.92652,0.88427,0.91005,0.71521,0.66321,0.41992,0.9582,0.0007228,0.0007228,0.0007228
86,1140.24,0.48709,0.32554,0.83767,0.90453,0.85663,0.89772,0.68684,0.7045,0.51257,1.01486,0.0007195,0.0007195,0.0007195
87,1151.85,0.48991,0.33384,0.84448,0.907,0.85895,0.89311,0.7048,0.688,0.40654,1.02405,0.0007162,0.0007162,0.0007162
88,1165.82,0.47768,0.32078,0.84114,0.91335,0.87703,0.90457,0.71941,0.69662,0.41756,1.01658,0.0007129,0.0007129,0.0007129
89,1179.9,0.46292,0.31346,0.83747,0.92017,0.87523,0.90512,0.70031,0.70133,0.41296,1.01517,0.0007096,0.0007096,0.0007096
90,1191.64,0.47472,0.32031,0.8383,0.94056,0.85172,0.91256,0.69874,0.74546,0.43067,1.03178,0.0007063,0.0007063,0.0007063
91,1203.66,0.47272,0.31834,0.83749,0.95372,0.86799,0.91379,0.72971,0.65447,0.40239,0.98918,0.000703,0.000703,0.000703
92,1216.18,0.47563,0.32345,0.83923,0.93656,0.85353,0.90677,0.71197,0.68924,0.39718,1.0055,0.0006997,0.0006997,0.0006997
93,1227.95,0.46929,0.31942,0.8356,0.90919,0.86907,0.88844,0.68078,0.71498,0.4308,1.03819,0.0006964,0.0006964,0.0006964
94,1240.14,0.47526,0.32685,0.84088,0.9424,0.85533,0.90051,0.69235,0.719,0.46186,1.00832,0.0006931,0.0006931,0.0006931
95,1252.74,0.48414,0.32903,0.84224,0.92802,0.83933,0.89681,0.70984,0.71585,0.42711,0.99938,0.0006898,0.0006898,0.0006898
96,1265.32,0.47906,0.3228,0.84473,0.90319,0.86043,0.91377,0.71846,0.73123,0.42424,1.02599,0.0006865,0.0006865,0.0006865
97,1280.87,0.48515,0.32743,0.84377,0.92493,0.89127,0.91324,0.70149,0.68474,0.42236,1.00245,0.0006832,0.0006832,0.0006832
98,1297.05,0.47635,0.31794,0.84276,0.95706,0.88608,0.92523,0.70832,0.72985,0.38181,1.0252,0.0006799,0.0006799,0.0006799
99,1310.21,0.48074,0.3315,0.83991,0.93131,0.87342,0.91162,0.69733,0.73048,0.39416,1.04901,0.0006766,0.0006766,0.0006766
100,1324.04,0.4728,0.31981,0.8406,0.90226,0.86804,0.89342,0.69261,0.71012,0.40061,1.03366,0.0006733,0.0006733,0.0006733
101,1337.56,0.47076,0.31483,0.83844,0.91424,0.85353,0.90281,0.712,0.69518,0.41087,1.01541,0.00067,0.00067,0.00067
102,1349.55,0.46218,0.32041,0.8428,0.92492,0.84655,0.89672,0.69095,0.71203,0.41839,1.03681,0.0006667,0.0006667,0.0006667
103,1361.88,0.47578,0.32417,0.84119,0.9111,0.8698,0.8834,0.66869,0.72213,0.44994,1.06049,0.0006634,0.0006634,0.0006634
104,1373.19,0.44611,0.3066,0.83246,0.91481,0.86257,0.88833,0.6986,0.69694,0.41254,1.05253,0.0006601,0.0006601,0.0006601
105,1386.37,0.46857,0.3156,0.83695,0.92071,0.86092,0.89771,0.71171,0.67548,0.38121,1.03343,0.0006568,0.0006568,0.0006568
106,1398.46,0.45919,0.30846,0.83651,0.91555,0.86076,0.90131,0.70325,0.7458,0.40659,1.08415,0.0006535,0.0006535,0.0006535
107,1411.68,0.45874,0.3063,0.83803,0.90968,0.8924,0.91799,0.71584,0.69706,0.38962,1.00431,0.0006502,0.0006502,0.0006502
108,1424.53,0.4754,0.31973,0.84394,0.92331,0.87703,0.89881,0.67953,0.73075,0.41059,1.05216,0.0006469,0.0006469,0.0006469
109,1438.7,0.46546,0.31341,0.83591,0.92759,0.87884,0.90256,0.71366,0.69365,0.39958,0.992,0.0006436,0.0006436,0.0006436
110,1452.06,0.45854,0.31392,0.84118,0.93255,0.87502,0.91568,0.70022,0.72925,0.41023,1.00777,0.0006403,0.0006403,0.0006403
111,1465.5,0.44592,0.30402,0.82915,0.94543,0.88427,0.90657,0.70177,0.7009,0.3977,1.0271,0.000637,0.000637,0.000637
112,1477.06,0.45529,0.31071,0.83851,0.9054,0.87161,0.8999,0.69202,0.7317,0.40164,1.04734,0.0006337,0.0006337,0.0006337
113,1488.71,0.46451,0.31103,0.83599,0.91115,0.87884,0.90566,0.70506,0.73948,0.37495,1.05174,0.0006304,0.0006304,0.0006304
114,1500.81,0.46996,0.31577,0.83512,0.9083,0.87342,0.89372,0.68722,0.74065,0.42079,1.06653,0.0006271,0.0006271,0.0006271
115,1513.38,0.45662,0.31001,0.83788,0.91057,0.8698,0.88247,0.68998,0.70139,0.40224,1.0493,0.0006238,0.0006238,0.0006238
116,1524.08,0.45388,0.31186,0.83535,0.91681,0.87342,0.89354,0.72169,0.66688,0.40111,0.97052,0.0006205,0.0006205,0.0006205
117,1536.61,0.45111,0.30597,0.83164,0.93357,0.88948,0.91159,0.69986,0.71294,0.39073,1.02112,0.0006172,0.0006172,0.0006172
118,1549.47,0.4345,0.2954,0.82631,0.9232,0.87161,0.89594,0.69382,0.70477,0.40546,1.03334,0.0006139,0.0006139,0.0006139
119,1562.12,0.44801,0.30341,0.83352,0.91578,0.86512,0.89412,0.70999,0.69146,0.43679,1.00766,0.0006106,0.0006106,0.0006106
120,1573.48,0.46259,0.31746,0.84003,0.94086,0.86303,0.91118,0.71194,0.69432,0.40778,1.00319,0.0006073,0.0006073,0.0006073
121,1586.94,0.45296,0.31182,0.83985,0.93357,0.88946,0.90923,0.68917,0.71741,0.39807,1.06493,0.000604,0.000604,0.000604
122,1599.18,0.45678,0.30899,0.83971,0.92137,0.88788,0.90705,0.73081,0.65972,0.39474,0.96801,0.0006007,0.0006007,0.0006007
123,1612.03,0.4371,0.29534,0.829,0.93054,0.87161,0.89555,0.70609,0.68901,0.40615,1.02068,0.0005974,0.0005974,0.0005974
124,1623.18,0.44696,0.30177,0.82855,0.89859,0.88131,0.90181,0.69312,0.70231,0.40449,1.03315,0.0005941,0.0005941,0.0005941
125,1635.74,0.42877,0.29038,0.82506,0.92344,0.87241,0.89231,0.68589,0.71746,0.42029,1.054,0.0005908,0.0005908,0.0005908
126,1646.86,0.43511,0.29468,0.82365,0.92015,0.87703,0.90056,0.67951,0.72235,0.42416,1.05745,0.0005875,0.0005875,0.0005875
127,1659.88,0.43624,0.29691,0.833,0.93942,0.87703,0.91105,0.72035,0.67245,0.40004,1.0088,0.0005842,0.0005842,0.0005842
128,1673.21,0.44243,0.29824,0.83473,0.91167,0.89331,0.9076,0.70523,0.71986,0.39723,1.02448,0.0005809,0.0005809,0.0005809
129,1684.86,0.44449,0.30788,0.8389,0.92457,0.88969,0.90368,0.70112,0.68756,0.41945,1.01377,0.0005776,0.0005776,0.0005776
130,1698.56,0.43999,0.30379,0.83098,0.92088,0.8915,0.90796,0.70892,0.67524,0.39043,1.01174,0.0005743,0.0005743,0.0005743
131,1711.07,0.43848,0.29905,0.82695,0.92732,0.87679,0.89832,0.69459,0.69666,0.40026,1.04369,0.000571,0.000571,0.000571
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 34.7021 0.99692 3.11052 0.95462 0 0 0 0 1.96969 5.09543 1.66347 0.07624 0.00024 0.00024
3 2 58.1564 0.88533 0.81005 0.95966 0 0 0 0 1.13737 5.66392 1.11577 0.0514884 0.000488383 0.000488383
4 3 92.7895 0.83799 0.71399 0.94176 0.61429 0.46655 0.55241 0.41655 0.98618 4.08179 1.04621 0.0267351 0.000735116 0.000735116
5 4 105.051 0.79628 0.66035 0.92157 0.81025 0.49367 0.6697 0.38789 1.35581 2.29532 1.21563 0.0019802 0.000980199 0.000980199
6 5 129.478 0.81136 0.63385 0.94036 0.90182 0.76404 0.81811 0.543 1.13814 0.99886 1.19084 0.0009868 0.0009868 0.0009868
7 6 141.623 0.77441 0.59174 0.92343 0.79664 0.72963 0.70525 0.45371 1.09478 1.06992 1.22257 0.0009835 0.0009835 0.0009835
8 7 154.902 0.77167 0.57802 0.91993 0.8682 0.83183 0.86792 0.63549 0.83551 0.70814 0.9794 0.0009802 0.0009802 0.0009802
9 8 169.038 0.74894 0.55503 0.91538 0.819 0.76492 0.80273 0.54722 0.98313 0.94247 1.06882 0.0009769 0.0009769 0.0009769
10 9 181.969 0.72558 0.5317 0.90209 0.87925 0.8481 0.8802 0.66413 0.84099 0.61761 0.99128 0.0009736 0.0009736 0.0009736
11 10 195.299 0.7306 0.51875 0.91204 0.8461 0.82098 0.84278 0.60296 0.8736 0.70076 1.05043 0.0009703 0.0009703 0.0009703
12 11 206.919 0.71221 0.52812 0.90491 0.89736 0.85714 0.89136 0.66282 0.8198 0.87756 0.98645 0.000967 0.000967 0.000967
13 12 217.7 0.68637 0.49626 0.89712 0.89841 0.83363 0.8516 0.6221 0.8753 0.73016 1.09195 0.0009637 0.0009637 0.0009637
14 13 228.845 0.66379 0.47568 0.8969 0.87593 0.84259 0.82557 0.58133 0.86205 0.68953 1.09495 0.0009604 0.0009604 0.0009604
15 14 239.297 0.68917 0.49114 0.89622 0.87716 0.86511 0.86626 0.63591 0.81925 0.61207 1.06045 0.0009571 0.0009571 0.0009571
16 15 250.335 0.67633 0.47741 0.89762 0.89996 0.83183 0.86878 0.63899 0.75256 0.64579 0.96647 0.0009538 0.0009538 0.0009538
17 16 261.353 0.63476 0.46476 0.8834 0.88932 0.87182 0.88096 0.66666 0.77169 0.5558 1.01473 0.0009505 0.0009505 0.0009505
18 17 272.608 0.65515 0.46115 0.88384 0.8921 0.83363 0.86032 0.62239 0.83968 0.87876 1.07603 0.0009472 0.0009472 0.0009472
19 18 284.717 0.64001 0.45228 0.87949 0.88447 0.81555 0.85197 0.63428 0.82735 0.59532 1.03181 0.0009439 0.0009439 0.0009439
20 19 296.522 0.62268 0.4525 0.88487 0.91193 0.84262 0.88032 0.64832 0.79121 0.61895 1.03754 0.0009406 0.0009406 0.0009406
21 20 309.592 0.63448 0.44433 0.88512 0.87666 0.82261 0.81656 0.55511 0.95443 0.65184 1.22882 0.0009373 0.0009373 0.0009373
22 21 322.182 0.63823 0.44304 0.88037 0.89522 0.85172 0.86106 0.60464 0.86042 0.51554 1.10164 0.000934 0.000934 0.000934
23 22 336.13 0.6077 0.4324 0.8729 0.89107 0.85714 0.84127 0.61986 0.81008 0.57466 1.06811 0.0009307 0.0009307 0.0009307
24 23 350.28 0.6207 0.43229 0.87345 0.9104 0.83183 0.8714 0.6628 0.78211 0.51027 1.03652 0.0009274 0.0009274 0.0009274
25 24 363.862 0.61806 0.43114 0.87883 0.88748 0.8415 0.87401 0.64248 0.81787 0.49065 1.08271 0.0009241 0.0009241 0.0009241
26 25 377.203 0.61985 0.43001 0.87774 0.90222 0.83183 0.87338 0.6879 0.68979 1.01589 0.94388 0.0009208 0.0009208 0.0009208
27 26 390.499 0.61656 0.42297 0.87265 0.93582 0.86799 0.9013 0.67935 0.76562 0.49424 0.98975 0.0009175 0.0009175 0.0009175
28 27 400.903 0.59142 0.40623 0.86666 0.92075 0.84629 0.88782 0.65838 0.77885 0.49803 1.04222 0.0009142 0.0009142 0.0009142
29 28 410.675 0.61042 0.41839 0.86873 0.90809 0.86257 0.87778 0.6555 0.76453 0.49995 1.00262 0.0009109 0.0009109 0.0009109
30 29 420.742 0.60576 0.41804 0.87656 0.91377 0.86235 0.88164 0.6601 0.73928 0.50308 0.99722 0.0009076 0.0009076 0.0009076
31 30 430.739 0.60603 0.41587 0.87676 0.89383 0.85254 0.89738 0.70005 0.71229 0.57312 0.94632 0.0009043 0.0009043 0.0009043
32 31 441.256 0.58791 0.41053 0.8654 0.90738 0.86618 0.88465 0.66675 0.76349 0.49064 1.0192 0.000901 0.000901 0.000901
33 32 451.956 0.58351 0.401 0.86283 0.91953 0.88854 0.90063 0.67086 0.78939 0.45019 1.0224 0.0008977 0.0008977 0.0008977
34 33 462.051 0.58838 0.41156 0.86788 0.92141 0.848 0.9108 0.69488 0.76778 0.49542 0.97043 0.0008944 0.0008944 0.0008944
35 34 472.298 0.5913 0.4086 0.8747 0.91674 0.85612 0.88607 0.64755 0.76797 0.52436 1.02984 0.0008911 0.0008911 0.0008911
36 35 484.125 0.57349 0.40808 0.865 0.90827 0.84156 0.88382 0.65875 0.76037 0.52884 1.00972 0.0008878 0.0008878 0.0008878
37 36 500.707 0.57406 0.39802 0.86615 0.90859 0.86618 0.89931 0.67024 0.76235 0.47401 1.03001 0.0008845 0.0008845 0.0008845
38 37 516.541 0.5699 0.39999 0.8673 0.90577 0.8698 0.87365 0.62141 0.80525 0.53512 1.10353 0.0008812 0.0008812 0.0008812
39 38 532.623 0.54216 0.38326 0.85993 0.9205 0.87936 0.9027 0.68621 0.76165 0.46101 1.01179 0.0008779 0.0008779 0.0008779
40 39 543.733 0.5696 0.40019 0.86481 0.92391 0.87834 0.90979 0.70101 0.72586 0.44776 0.99587 0.0008746 0.0008746 0.0008746
41 40 558.525 0.56581 0.39388 0.8605 0.90462 0.87469 0.90686 0.7134 0.70046 0.43642 0.9893 0.0008713 0.0008713 0.0008713
42 41 571.17 0.56399 0.38437 0.86165 0.91521 0.88608 0.90844 0.70078 0.68867 0.45458 0.96522 0.000868 0.000868 0.000868
43 42 584.833 0.56076 0.39028 0.85887 0.90656 0.88608 0.91085 0.7337 0.70138 0.42411 0.95804 0.0008647 0.0008647 0.0008647
44 43 598.723 0.54489 0.38042 0.85394 0.90197 0.86618 0.89149 0.68332 0.75302 0.47361 1.01357 0.0008614 0.0008614 0.0008614
45 44 611.639 0.55044 0.37233 0.85598 0.93751 0.87884 0.9118 0.72068 0.68576 0.43353 0.95107 0.0008581 0.0008581 0.0008581
46 45 624.771 0.55095 0.37455 0.85631 0.90754 0.85199 0.87837 0.68771 0.70368 0.4779 0.9985 0.0008548 0.0008548 0.0008548
47 46 640.54 0.56156 0.38495 0.86831 0.90819 0.85859 0.87414 0.64421 0.77426 0.45845 1.048 0.0008515 0.0008515 0.0008515
48 47 655.686 0.5547 0.37727 0.85514 0.91334 0.87665 0.90121 0.68988 0.76153 0.45147 1.04075 0.0008482 0.0008482 0.0008482
49 48 669.206 0.56647 0.38913 0.86151 0.90202 0.88427 0.89978 0.66623 0.76016 0.46199 1.03915 0.0008449 0.0008449 0.0008449
50 49 684.126 0.53937 0.36699 0.85849 0.89649 0.8457 0.89009 0.67258 0.77955 0.45769 1.07779 0.0008416 0.0008416 0.0008416
51 50 698.461 0.54831 0.36492 0.85622 0.89782 0.88065 0.90287 0.71383 0.697 0.42852 1.00059 0.0008383 0.0008383 0.0008383
52 51 713.645 0.54613 0.36397 0.85931 0.90096 0.87703 0.88793 0.67727 0.75559 0.47143 1.01223 0.000835 0.000835 0.000835
53 52 731.977 0.54465 0.37022 0.86009 0.90715 0.88338 0.89602 0.68439 0.7357 0.4338 1.01677 0.0008317 0.0008317 0.0008317
54 53 743.045 0.52914 0.37523 0.8544 0.89495 0.88246 0.90087 0.69102 0.73401 0.46935 1.00157 0.0008284 0.0008284 0.0008284
55 54 754.526 0.52389 0.36796 0.85348 0.90375 0.87703 0.90136 0.68208 0.72443 0.4251 0.99654 0.0008251 0.0008251 0.0008251
56 55 766.145 0.53695 0.37007 0.8593 0.92242 0.88065 0.90644 0.69544 0.72786 0.43352 1.01616 0.0008218 0.0008218 0.0008218
57 56 777.738 0.53298 0.37268 0.85449 0.92986 0.87884 0.91812 0.70779 0.72376 0.4199 1.02072 0.0008185 0.0008185 0.0008185
58 57 789.079 0.52278 0.36385 0.84482 0.93893 0.86187 0.90375 0.69383 0.70366 0.44424 0.99126 0.0008152 0.0008152 0.0008152
59 58 800.046 0.53586 0.36782 0.8521 0.93618 0.85172 0.88593 0.6846 0.72546 0.48908 1.02899 0.0008119 0.0008119 0.0008119
60 59 812.447 0.51175 0.3556 0.84435 0.91532 0.87955 0.89698 0.66439 0.78617 0.44624 1.08033 0.0008086 0.0008086 0.0008086
61 60 822.502 0.52929 0.35839 0.8547 0.91753 0.88788 0.90612 0.67992 0.7899 0.43338 1.04613 0.0008053 0.0008053 0.0008053
62 61 835.085 0.53648 0.36334 0.85282 0.94742 0.88427 0.92062 0.70481 0.72008 0.4443 0.96516 0.000802 0.000802 0.000802
63 62 846.861 0.51907 0.35473 0.84629 0.92732 0.87703 0.89766 0.68322 0.70787 0.47261 1.01025 0.0007987 0.0007987 0.0007987
64 63 858.564 0.51051 0.3523 0.85127 0.92886 0.88427 0.90168 0.70173 0.69482 0.46427 0.97892 0.0007954 0.0007954 0.0007954
65 64 870.064 0.52071 0.3634 0.85274 0.92049 0.8698 0.88206 0.68041 0.71646 0.48517 1.00591 0.0007921 0.0007921 0.0007921
66 65 882.799 0.50793 0.34813 0.85074 0.9073 0.88065 0.90013 0.69868 0.70316 0.45048 0.98568 0.0007888 0.0007888 0.0007888
67 66 896.029 0.51142 0.34745 0.8548 0.90485 0.85353 0.89757 0.69137 0.73512 0.42862 1.05296 0.0007855 0.0007855 0.0007855
68 67 907.816 0.51635 0.34536 0.85241 0.92261 0.88388 0.90844 0.7153 0.67732 0.41922 1.00051 0.0007822 0.0007822 0.0007822
69 68 918.958 0.50808 0.35686 0.84852 0.94178 0.86257 0.90453 0.66669 0.76668 0.43723 1.07557 0.0007789 0.0007789 0.0007789
70 69 931.244 0.4998 0.33677 0.84352 0.92619 0.88496 0.90317 0.68321 0.75844 0.42174 1.04478 0.0007756 0.0007756 0.0007756
71 70 942.872 0.52196 0.35231 0.8499 0.92503 0.87021 0.90893 0.69302 0.72319 0.45712 1.01505 0.0007723 0.0007723 0.0007723
72 71 955.449 0.50125 0.33892 0.84865 0.91341 0.87752 0.90688 0.69514 0.723 0.41792 1.03109 0.000769 0.000769 0.000769
73 72 968.156 0.49979 0.34101 0.84551 0.93439 0.89331 0.9212 0.72195 0.69226 0.40073 0.97614 0.0007657 0.0007657 0.0007657
74 73 980.354 0.51371 0.34288 0.84347 0.93253 0.87475 0.89787 0.69665 0.71931 0.41821 0.99889 0.0007624 0.0007624 0.0007624
75 74 991.234 0.50302 0.33922 0.84588 0.90984 0.87342 0.90602 0.69639 0.7558 0.42219 1.05959 0.0007591 0.0007591 0.0007591
76 75 1004.05 0.49584 0.34472 0.84401 0.92344 0.86438 0.8959 0.68855 0.72366 0.43524 1.02242 0.0007558 0.0007558 0.0007558
77 76 1014.82 0.49028 0.33776 0.84343 0.92713 0.89725 0.91799 0.70346 0.7291 0.41195 1.01147 0.0007525 0.0007525 0.0007525
78 77 1027.12 0.49405 0.32601 0.84625 0.92347 0.89467 0.90552 0.69395 0.72762 0.41947 1.04666 0.0007492 0.0007492 0.0007492
79 78 1039.23 0.49736 0.33638 0.84642 0.92874 0.87703 0.91351 0.69864 0.70537 0.42223 1.02199 0.0007459 0.0007459 0.0007459
80 79 1051 0.49819 0.34083 0.84315 0.89828 0.88969 0.91238 0.69457 0.72477 0.42391 1.02981 0.0007426 0.0007426 0.0007426
81 80 1066.14 0.51095 0.3441 0.85009 0.87679 0.87703 0.87828 0.65401 0.76728 0.49605 1.10681 0.0007393 0.0007393 0.0007393
82 81 1079.16 0.50392 0.3406 0.84965 0.91518 0.87161 0.91496 0.74287 0.65844 0.3773 0.98431 0.000736 0.000736 0.000736
83 82 1090.51 0.50999 0.33647 0.84333 0.92449 0.88065 0.91303 0.72863 0.64829 0.40398 0.97032 0.0007327 0.0007327 0.0007327
84 83 1103.58 0.48732 0.33154 0.84668 0.90863 0.88114 0.89377 0.69624 0.71363 0.42001 1.02309 0.0007294 0.0007294 0.0007294
85 84 1115 0.5065 0.33619 0.84501 0.9053 0.90778 0.91101 0.69981 0.71369 0.39276 1.01418 0.0007261 0.0007261 0.0007261
86 85 1128.82 0.48695 0.32756 0.83675 0.92652 0.88427 0.91005 0.71521 0.66321 0.41992 0.9582 0.0007228 0.0007228 0.0007228
87 86 1140.24 0.48709 0.32554 0.83767 0.90453 0.85663 0.89772 0.68684 0.7045 0.51257 1.01486 0.0007195 0.0007195 0.0007195
88 87 1151.85 0.48991 0.33384 0.84448 0.907 0.85895 0.89311 0.7048 0.688 0.40654 1.02405 0.0007162 0.0007162 0.0007162
89 88 1165.82 0.47768 0.32078 0.84114 0.91335 0.87703 0.90457 0.71941 0.69662 0.41756 1.01658 0.0007129 0.0007129 0.0007129
90 89 1179.9 0.46292 0.31346 0.83747 0.92017 0.87523 0.90512 0.70031 0.70133 0.41296 1.01517 0.0007096 0.0007096 0.0007096
91 90 1191.64 0.47472 0.32031 0.8383 0.94056 0.85172 0.91256 0.69874 0.74546 0.43067 1.03178 0.0007063 0.0007063 0.0007063
92 91 1203.66 0.47272 0.31834 0.83749 0.95372 0.86799 0.91379 0.72971 0.65447 0.40239 0.98918 0.000703 0.000703 0.000703
93 92 1216.18 0.47563 0.32345 0.83923 0.93656 0.85353 0.90677 0.71197 0.68924 0.39718 1.0055 0.0006997 0.0006997 0.0006997
94 93 1227.95 0.46929 0.31942 0.8356 0.90919 0.86907 0.88844 0.68078 0.71498 0.4308 1.03819 0.0006964 0.0006964 0.0006964
95 94 1240.14 0.47526 0.32685 0.84088 0.9424 0.85533 0.90051 0.69235 0.719 0.46186 1.00832 0.0006931 0.0006931 0.0006931
96 95 1252.74 0.48414 0.32903 0.84224 0.92802 0.83933 0.89681 0.70984 0.71585 0.42711 0.99938 0.0006898 0.0006898 0.0006898
97 96 1265.32 0.47906 0.3228 0.84473 0.90319 0.86043 0.91377 0.71846 0.73123 0.42424 1.02599 0.0006865 0.0006865 0.0006865
98 97 1280.87 0.48515 0.32743 0.84377 0.92493 0.89127 0.91324 0.70149 0.68474 0.42236 1.00245 0.0006832 0.0006832 0.0006832
99 98 1297.05 0.47635 0.31794 0.84276 0.95706 0.88608 0.92523 0.70832 0.72985 0.38181 1.0252 0.0006799 0.0006799 0.0006799
100 99 1310.21 0.48074 0.3315 0.83991 0.93131 0.87342 0.91162 0.69733 0.73048 0.39416 1.04901 0.0006766 0.0006766 0.0006766
101 100 1324.04 0.4728 0.31981 0.8406 0.90226 0.86804 0.89342 0.69261 0.71012 0.40061 1.03366 0.0006733 0.0006733 0.0006733
102 101 1337.56 0.47076 0.31483 0.83844 0.91424 0.85353 0.90281 0.712 0.69518 0.41087 1.01541 0.00067 0.00067 0.00067
103 102 1349.55 0.46218 0.32041 0.8428 0.92492 0.84655 0.89672 0.69095 0.71203 0.41839 1.03681 0.0006667 0.0006667 0.0006667
104 103 1361.88 0.47578 0.32417 0.84119 0.9111 0.8698 0.8834 0.66869 0.72213 0.44994 1.06049 0.0006634 0.0006634 0.0006634
105 104 1373.19 0.44611 0.3066 0.83246 0.91481 0.86257 0.88833 0.6986 0.69694 0.41254 1.05253 0.0006601 0.0006601 0.0006601
106 105 1386.37 0.46857 0.3156 0.83695 0.92071 0.86092 0.89771 0.71171 0.67548 0.38121 1.03343 0.0006568 0.0006568 0.0006568
107 106 1398.46 0.45919 0.30846 0.83651 0.91555 0.86076 0.90131 0.70325 0.7458 0.40659 1.08415 0.0006535 0.0006535 0.0006535
108 107 1411.68 0.45874 0.3063 0.83803 0.90968 0.8924 0.91799 0.71584 0.69706 0.38962 1.00431 0.0006502 0.0006502 0.0006502
109 108 1424.53 0.4754 0.31973 0.84394 0.92331 0.87703 0.89881 0.67953 0.73075 0.41059 1.05216 0.0006469 0.0006469 0.0006469
110 109 1438.7 0.46546 0.31341 0.83591 0.92759 0.87884 0.90256 0.71366 0.69365 0.39958 0.992 0.0006436 0.0006436 0.0006436
111 110 1452.06 0.45854 0.31392 0.84118 0.93255 0.87502 0.91568 0.70022 0.72925 0.41023 1.00777 0.0006403 0.0006403 0.0006403
112 111 1465.5 0.44592 0.30402 0.82915 0.94543 0.88427 0.90657 0.70177 0.7009 0.3977 1.0271 0.000637 0.000637 0.000637
113 112 1477.06 0.45529 0.31071 0.83851 0.9054 0.87161 0.8999 0.69202 0.7317 0.40164 1.04734 0.0006337 0.0006337 0.0006337
114 113 1488.71 0.46451 0.31103 0.83599 0.91115 0.87884 0.90566 0.70506 0.73948 0.37495 1.05174 0.0006304 0.0006304 0.0006304
115 114 1500.81 0.46996 0.31577 0.83512 0.9083 0.87342 0.89372 0.68722 0.74065 0.42079 1.06653 0.0006271 0.0006271 0.0006271
116 115 1513.38 0.45662 0.31001 0.83788 0.91057 0.8698 0.88247 0.68998 0.70139 0.40224 1.0493 0.0006238 0.0006238 0.0006238
117 116 1524.08 0.45388 0.31186 0.83535 0.91681 0.87342 0.89354 0.72169 0.66688 0.40111 0.97052 0.0006205 0.0006205 0.0006205
118 117 1536.61 0.45111 0.30597 0.83164 0.93357 0.88948 0.91159 0.69986 0.71294 0.39073 1.02112 0.0006172 0.0006172 0.0006172
119 118 1549.47 0.4345 0.2954 0.82631 0.9232 0.87161 0.89594 0.69382 0.70477 0.40546 1.03334 0.0006139 0.0006139 0.0006139
120 119 1562.12 0.44801 0.30341 0.83352 0.91578 0.86512 0.89412 0.70999 0.69146 0.43679 1.00766 0.0006106 0.0006106 0.0006106
121 120 1573.48 0.46259 0.31746 0.84003 0.94086 0.86303 0.91118 0.71194 0.69432 0.40778 1.00319 0.0006073 0.0006073 0.0006073
122 121 1586.94 0.45296 0.31182 0.83985 0.93357 0.88946 0.90923 0.68917 0.71741 0.39807 1.06493 0.000604 0.000604 0.000604
123 122 1599.18 0.45678 0.30899 0.83971 0.92137 0.88788 0.90705 0.73081 0.65972 0.39474 0.96801 0.0006007 0.0006007 0.0006007
124 123 1612.03 0.4371 0.29534 0.829 0.93054 0.87161 0.89555 0.70609 0.68901 0.40615 1.02068 0.0005974 0.0005974 0.0005974
125 124 1623.18 0.44696 0.30177 0.82855 0.89859 0.88131 0.90181 0.69312 0.70231 0.40449 1.03315 0.0005941 0.0005941 0.0005941
126 125 1635.74 0.42877 0.29038 0.82506 0.92344 0.87241 0.89231 0.68589 0.71746 0.42029 1.054 0.0005908 0.0005908 0.0005908
127 126 1646.86 0.43511 0.29468 0.82365 0.92015 0.87703 0.90056 0.67951 0.72235 0.42416 1.05745 0.0005875 0.0005875 0.0005875
128 127 1659.88 0.43624 0.29691 0.833 0.93942 0.87703 0.91105 0.72035 0.67245 0.40004 1.0088 0.0005842 0.0005842 0.0005842
129 128 1673.21 0.44243 0.29824 0.83473 0.91167 0.89331 0.9076 0.70523 0.71986 0.39723 1.02448 0.0005809 0.0005809 0.0005809
130 129 1684.86 0.44449 0.30788 0.8389 0.92457 0.88969 0.90368 0.70112 0.68756 0.41945 1.01377 0.0005776 0.0005776 0.0005776
131 130 1698.56 0.43999 0.30379 0.83098 0.92088 0.8915 0.90796 0.70892 0.67524 0.39043 1.01174 0.0005743 0.0005743 0.0005743
132 131 1711.07 0.43848 0.29905 0.82695 0.92732 0.87679 0.89832 0.69459 0.69666 0.40026 1.04369 0.000571 0.000571 0.000571

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swoon_detect/swoon_detect.yaml
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# 데이터셋 경로
train: /home/cuuva/experiment/datasets/swoon_detection/Training/images/
val: /home/cuuva/experiment/datasets/swoon_detection/Validation/images/
# 클래스 수
nc: 1
# 클래스 이름
names: ['swoon']

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swoon_detect/yolo_exp.ipynb
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ultralytics

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Subproject commit 25738f5195fb98021521e733f406208ed494ffa3

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vis5class_exp/vis5class.yaml
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# path: /home/cuuva/experiment/datasets/VisDrone # 데이터 경로
train: /home/cuuva/experiment/datasets/vis5class/images/train
val: /home/cuuva/experiment/datasets/vis5class/images/val
test: /home/cuuva/experiment/datasets/vis5class/images/test
# nc: 7
nc: 5
names: ['person','car', 'truck', 'bus', 'motor']

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vis5class_exp/vis5class_exp.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6b410dab",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f66929b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e5e2ab7b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"🔍 Checking split: train (files: 6471)\n",
"\n",
"🔍 Checking split: val (files: 548)\n",
"\n",
"🔍 Checking split: test (files: 1610)\n",
"\n",
"📌 클래스 통계 결과:\n",
" - Class 0: 147747 개\n",
" - Class 1: 219707 개\n",
" - Class 2: 16284 개\n",
" - Class 3: 9117 개\n",
" - Class 4: 40378 개\n",
"\n",
"총 클래스 종류: 5\n"
]
}
],
"source": [
"import os\n",
"from collections import Counter\n",
"\n",
"label_root = \"/home/cuuva/experiment/datasets/vis5class/labels\"\n",
"splits = [\"train\", \"val\", \"test\"]\n",
"\n",
"class_counter = Counter()\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" \n",
" # 라벨 txt 파일 탐색\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
" \n",
" print(f\"\\n🔍 Checking split: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip(): # 빈 줄 제거\n",
" class_id = line.split()[0] # 첫번째 값 = 클래스\n",
" class_counter[class_id] += 1\n",
"\n",
"# 최종 결과 출력\n",
"print(\"\\n📌 클래스 통계 결과:\")\n",
"for cls, count in sorted(class_counter.items(), key=lambda x: int(x[0])):\n",
" print(f\" - Class {cls}: {count} 개\")\n",
"\n",
"print(f\"\\n총 클래스 종류: {len(class_counter)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6bb38c03",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m.pt to 'yolov8m.pt': 100% ━━━━━━━━━━━━ 49.7MB 70.1MB/s 0.7s0.7s<0.0s\n",
"New https://pypi.org/project/ultralytics/8.3.234 available 😃 Update with 'pip install -U ultralytics'\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/vis5class_exp/vis5class.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=300, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=5class, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=40, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=vis5class_v8m, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=5\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2] \n",
" 1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2] \n",
" 2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True] \n",
" 3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] \n",
" 4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True] \n",
" 5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2] \n",
" 6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True] \n",
" 7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2] \n",
" 8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True] \n",
" 9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2] \n",
" 16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2] \n",
" 19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] \n",
" 22 [15, 18, 21] 1 3778591 ultralytics.nn.modules.head.Detect [5, [192, 384, 576]] \n",
"Model summary: 169 layers, 25,859,215 parameters, 25,859,199 gradients, 79.1 GFLOPs\n",
"\n",
"Transferred 469/475 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 73.7MB/s 0.1s\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 12140.0±4222.1 MB/s, size: 260.7 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/vis5class/labels/train... 6471 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6471/6471 5.3Kit/s 1.2s0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999972_00000_d_0000158.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999981_00000_d_0000047.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/vis5class/labels/train.cache\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 0.25G reserved, 0.24G allocated, 30.85G free\n",
" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
" 25859215 79.08 2.382 28.74 146.3 (1, 3, 640, 640) list\n",
" 25859215 158.2 3.811 9.848 33.63 (2, 3, 640, 640) list\n",
" 25859215 316.3 5.216 13.39 46.45 (4, 3, 640, 640) list\n",
" 25859215 632.6 8.735 26.08 69.81 (8, 3, 640, 640) list\n",
" 25859215 1265 12.298 27.03 116.4 (16, 3, 640, 640) list\n",
" 25859215 2531 24.285 54.71 238.4 (32, 3, 640, 640) list\n",
" 25859215 5061 28.001 116.9 470.2 (64, 3, 640, 640) list\n",
"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 34 for CUDA:0 19.26G/31.33G (61%) ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 12817.4±4106.6 MB/s, size: 235.0 KB)\n",
"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/vis5class/labels/train.cache... 6471 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6471/6471 18.5Mit/s 0.0s\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999972_00000_d_0000158.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999981_00000_d_0000047.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/vis5class/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 7032.9±4571.8 MB/s, size: 153.1 KB)\n",
"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/vis5class/labels/val... 548 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 548/548 4.2Kit/s 0.1s0.1s\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/vis5class/labels/val.cache\n",
"Plotting labels to /home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.00053125), 83 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1m/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class\u001b[0m\n",
"Starting training for 300 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 1/300 18.4G 1.445 1.19 0.9667 618 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 5.6it/s 1.6s0.2s\n",
" all 548 35895 0.526 0.312 0.326 0.191\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 2/300 20.8G 1.409 0.9562 0.9555 790 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:120.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.4it/s 1.4s0.2s\n",
" all 548 35895 0.536 0.354 0.386 0.226\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 3/300 18.8G 1.39 0.9126 0.9471 610 640: 100% ━━━━━━━━━━━━ 191/191 2.6it/s 1:120.3sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 5.4it/s 1.7s0.2s\n",
" all 548 35895 0.467 0.36 0.356 0.205\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 4/300 22G 1.378 0.8927 0.9436 1135 640: 100% ━━━━━━━━━━━━ 191/191 2.6it/s 1:120.3sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 4.7it/s 1.9s0.2s\n",
" all 548 35895 0.539 0.409 0.425 0.243\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 5/300 25.4G 1.349 0.8549 0.9388 714 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.7it/s 1.3s0.2s\n",
" all 548 35895 0.58 0.402 0.433 0.258\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 6/300 21.1G 1.319 0.8225 0.9321 640 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.7it/s 1.3s0.2s\n",
" all 548 35895 0.556 0.419 0.447 0.261\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 7/300 16.7G 1.31 0.8102 0.927 847 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.8it/s 1.3s0.2s\n",
" all 548 35895 0.578 0.417 0.444 0.26\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 8/300 22.1G 1.298 0.7965 0.925 836 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.572 0.42 0.454 0.267\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 9/300 23.8G 1.29 0.7875 0.9216 958 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.7it/s 1.4s0.2s\n",
" all 548 35895 0.623 0.433 0.477 0.284\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 10/300 21.9G 1.275 0.7749 0.92 845 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.8it/s 1.3s0.2s\n",
" all 548 35895 0.609 0.431 0.474 0.281\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 11/300 23.7G 1.266 0.7587 0.9175 857 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.618 0.442 0.482 0.289\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 12/300 23G 1.259 0.7559 0.9144 503 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.611 0.45 0.489 0.292\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 13/300 19.3G 1.253 0.7427 0.9112 1095 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.8it/s 1.3s0.2s\n",
" all 548 35895 0.601 0.449 0.486 0.286\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 14/300 20.9G 1.246 0.742 0.911 686 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.608 0.467 0.495 0.297\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 15/300 18.8G 1.241 0.7282 0.9084 598 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.648 0.451 0.504 0.304\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 16/300 20G 1.235 0.7257 0.9081 1140 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.628 0.464 0.512 0.305\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 17/300 21.8G 1.228 0.7206 0.9066 514 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.626 0.476 0.518 0.313\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 18/300 18.8G 1.231 0.7161 0.9061 902 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.621 0.466 0.506 0.306\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 19/300 16.6G 1.213 0.7062 0.904 832 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.612 0.48 0.518 0.316\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 20/300 18.7G 1.214 0.7021 0.903 716 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.622 0.484 0.516 0.312\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 21/300 19.1G 1.218 0.7065 0.9038 393 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.641 0.476 0.519 0.316\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 22/300 19G 1.199 0.6923 0.9001 815 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.622 0.471 0.516 0.315\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 23/300 18.9G 1.2 0.691 0.9 912 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.652 0.475 0.525 0.319\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 24/300 18.9G 1.192 0.6794 0.898 1114 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.643 0.487 0.528 0.323\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 25/300 22.8G 1.196 0.682 0.9 880 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.66 0.495 0.545 0.331\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 26/300 19G 1.187 0.6771 0.8983 1148 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.63 0.481 0.532 0.323\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 27/300 19.7G 1.19 0.6749 0.8966 1210 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.3sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.656 0.488 0.543 0.328\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 28/300 21.1G 1.191 0.6745 0.8952 947 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.623 0.503 0.531 0.324\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 29/300 19.8G 1.186 0.6704 0.8943 905 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.675 0.476 0.54 0.326\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 30/300 21.9G 1.179 0.6618 0.8955 560 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.672 0.489 0.54 0.331\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 31/300 19.1G 1.173 0.6644 0.8921 668 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.673 0.494 0.545 0.335\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 32/300 21.4G 1.178 0.6625 0.893 676 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.66 0.494 0.545 0.334\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 33/300 22.4G 1.168 0.653 0.8915 746 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.672 0.497 0.548 0.333\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 34/300 21.9G 1.162 0.6489 0.8903 892 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.651 0.492 0.54 0.329\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 35/300 22.9G 1.16 0.6461 0.8894 639 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.652 0.495 0.545 0.333\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 36/300 19.7G 1.162 0.6476 0.8903 1132 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.662 0.487 0.535 0.328\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 37/300 19.2G 1.156 0.6424 0.8889 966 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.669 0.504 0.55 0.337\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 38/300 19G 1.162 0.6471 0.8897 1098 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.663 0.509 0.552 0.337\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 39/300 19.6G 1.156 0.6399 0.8871 795 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.687 0.489 0.545 0.333\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 40/300 22.7G 1.149 0.6347 0.8869 1149 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.679 0.496 0.547 0.337\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 41/300 18.5G 1.145 0.6289 0.8848 800 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.677 0.491 0.551 0.34\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 42/300 21.2G 1.147 0.6306 0.8872 945 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.666 0.514 0.557 0.343\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 43/300 23.5G 1.147 0.6291 0.8861 772 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.677 0.514 0.56 0.345\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 44/300 20.1G 1.139 0.6218 0.8839 790 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.67 0.499 0.554 0.339\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 45/300 23G 1.142 0.6234 0.8834 1039 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.661 0.504 0.55 0.335\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 46/300 18.9G 1.142 0.6253 0.885 474 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.671 0.498 0.553 0.341\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 47/300 24.4G 1.13 0.6175 0.8819 761 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.679 0.494 0.551 0.339\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 48/300 19.3G 1.136 0.6174 0.8842 1335 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.678 0.508 0.56 0.344\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 49/300 22.5G 1.126 0.6114 0.8816 928 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.683 0.505 0.559 0.345\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 50/300 18.8G 1.13 0.6113 0.8809 636 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.675 0.509 0.56 0.346\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 51/300 19.1G 1.124 0.6089 0.8796 851 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.664 0.515 0.56 0.345\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 52/300 21.7G 1.124 0.6087 0.8811 822 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.685 0.511 0.568 0.347\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 53/300 21.5G 1.121 0.6047 0.8787 539 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.509 0.56 0.343\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 54/300 23.2G 1.118 0.6022 0.8775 794 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.518 0.568 0.348\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 55/300 21.1G 1.111 0.5961 0.8784 848 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.692 0.519 0.571 0.35\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 56/300 18.3G 1.111 0.593 0.8785 762 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.67 0.515 0.561 0.344\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 57/300 21.9G 1.105 0.5927 0.876 661 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.656 0.501 0.556 0.342\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 58/300 19.8G 1.109 0.5936 0.877 668 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.659 0.518 0.561 0.345\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 59/300 17.1G 1.112 0.5944 0.8769 934 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.668 0.53 0.568 0.349\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 60/300 22.3G 1.114 0.5946 0.8777 654 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.666 0.526 0.567 0.35\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 61/300 19.4G 1.106 0.5902 0.876 767 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.673 0.527 0.567 0.352\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 62/300 20.3G 1.109 0.5889 0.8754 842 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.694 0.517 0.57 0.352\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 63/300 21.5G 1.101 0.5854 0.8747 923 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.679 0.515 0.566 0.349\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 64/300 18.4G 1.1 0.5838 0.8746 659 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.698 0.518 0.571 0.352\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 65/300 21.1G 1.097 0.5852 0.8739 1194 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.671 0.527 0.571 0.35\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 66/300 19G 1.094 0.5793 0.8728 602 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.519 0.567 0.35\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 67/300 18.8G 1.093 0.5783 0.8722 798 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.522 0.571 0.351\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 68/300 20.9G 1.095 0.5794 0.8736 770 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.663 0.529 0.57 0.351\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 69/300 17G 1.088 0.575 0.8725 737 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.686 0.523 0.572 0.353\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 70/300 19.1G 1.08 0.569 0.8709 806 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.676 0.525 0.574 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 71/300 20.8G 1.09 0.5736 0.8729 866 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.677 0.529 0.573 0.353\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 72/300 20.5G 1.087 0.5741 0.871 639 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.696 0.521 0.575 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 73/300 19.1G 1.076 0.5661 0.8694 886 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.7 0.518 0.575 0.353\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 74/300 18.9G 1.083 0.5652 0.8698 773 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.524 0.573 0.352\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 75/300 19.4G 1.075 0.5646 0.8692 837 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.686 0.53 0.576 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 76/300 18.3G 1.085 0.5692 0.8706 912 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.704 0.512 0.572 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 77/300 18.7G 1.074 0.5624 0.8697 766 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.685 0.528 0.574 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 78/300 18.7G 1.072 0.5574 0.8686 816 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.703 0.524 0.574 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 79/300 19G 1.064 0.5525 0.8673 503 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.682 0.53 0.575 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 80/300 19.7G 1.073 0.5606 0.866 1023 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.678 0.536 0.576 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 81/300 19G 1.067 0.5565 0.8677 730 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.703 0.52 0.577 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 82/300 21.7G 1.058 0.551 0.8672 1342 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.691 0.523 0.575 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 83/300 21.1G 1.069 0.5587 0.8676 679 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.679 0.532 0.579 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 84/300 21G 1.062 0.5505 0.8675 1074 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.692 0.527 0.577 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 85/300 19.2G 1.062 0.5504 0.8666 579 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.707 0.517 0.573 0.352\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 86/300 20.9G 1.06 0.5507 0.865 746 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.693 0.527 0.574 0.353\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 87/300 19.2G 1.068 0.556 0.8667 1110 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.695 0.528 0.576 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 88/300 19.7G 1.055 0.5491 0.8649 686 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.689 0.532 0.575 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 89/300 19.3G 1.058 0.5491 0.8651 911 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.689 0.522 0.574 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 90/300 24.9G 1.06 0.5457 0.8637 892 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.69 0.527 0.573 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 91/300 19G 1.054 0.5454 0.8625 638 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.699 0.528 0.579 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 92/300 22.3G 1.056 0.5456 0.864 538 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.679 0.541 0.579 0.359\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 93/300 21.8G 1.057 0.5452 0.8633 828 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.688 0.531 0.575 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 94/300 18.3G 1.043 0.5369 0.8614 891 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.694 0.534 0.576 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 95/300 21.7G 1.037 0.5346 0.8609 885 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.696 0.53 0.579 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 96/300 19G 1.047 0.5392 0.8633 961 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.698 0.531 0.581 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 97/300 21G 1.045 0.5377 0.862 611 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.688 0.532 0.579 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 98/300 19.5G 1.042 0.5351 0.8637 1026 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.695 0.531 0.577 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 99/300 20.9G 1.04 0.5328 0.8602 550 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.692 0.532 0.577 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 100/300 24.8G 1.039 0.5343 0.861 669 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.69 0.526 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 101/300 21.1G 1.032 0.5306 0.8611 835 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.699 0.529 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 102/300 18.9G 1.037 0.5321 0.8609 943 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.699 0.525 0.578 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 103/300 16.9G 1.028 0.5275 0.8587 1033 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.699 0.523 0.576 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 104/300 19.4G 1.034 0.5272 0.8605 664 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.696 0.524 0.575 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 105/300 18.8G 1.034 0.5285 0.8597 795 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.686 0.531 0.574 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 106/300 19.5G 1.032 0.5272 0.8589 807 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.697 0.529 0.577 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 107/300 18.1G 1.03 0.5277 0.8602 519 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.688 0.531 0.577 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 108/300 19.2G 1.03 0.5272 0.8587 1017 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.695 0.524 0.575 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 109/300 20.2G 1.028 0.5258 0.8587 755 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.686 0.53 0.572 0.353\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 110/300 23.9G 1.019 0.5197 0.8564 594 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.681 0.53 0.574 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 111/300 21.2G 1.03 0.5242 0.8571 655 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.674 0.538 0.576 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 112/300 21.1G 1.017 0.5173 0.8562 667 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.676 0.537 0.576 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 113/300 22.1G 1.019 0.518 0.8554 702 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.689 0.528 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 114/300 20.8G 1.019 0.5173 0.8568 547 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.695 0.53 0.578 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 115/300 22.4G 1.015 0.5159 0.8547 700 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.682 0.535 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 116/300 21.6G 1.007 0.5125 0.8559 803 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.7 0.526 0.578 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 117/300 20.6G 1.009 0.5123 0.8544 878 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.702 0.522 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 118/300 18.1G 1.018 0.5164 0.8554 649 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.686 0.528 0.578 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 119/300 23G 1.009 0.512 0.8548 646 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.689 0.53 0.58 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 120/300 21.7G 1.007 0.5117 0.8556 1382 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.688 0.533 0.579 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 121/300 22.9G 1.008 0.51 0.8536 877 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.697 0.529 0.578 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 122/300 16.6G 1.009 0.5124 0.8543 720 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:100.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.7 0.528 0.579 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 123/300 23.7G 1.009 0.5111 0.8533 957 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.697 0.528 0.58 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 124/300 21.9G 1 0.5067 0.8533 641 640: 100% ━━━━━━━━━━━━ 191/191 2.6it/s 1:120.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.702 0.521 0.578 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 125/300 21G 1.004 0.5091 0.8546 1118 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:120.3sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.9it/s 1.3s0.2s\n",
" all 548 35895 0.694 0.525 0.577 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 126/300 19.8G 1.007 0.5086 0.8551 642 640: 100% ━━━━━━━━━━━━ 191/191 2.6it/s 1:130.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.3s0.2s\n",
" all 548 35895 0.689 0.53 0.578 0.357\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 127/300 20.9G 0.9983 0.5042 0.8513 732 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.69 0.529 0.578 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 128/300 18.4G 0.9943 0.5024 0.8525 541 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.701 0.523 0.577 0.356\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 129/300 19.5G 1.002 0.5059 0.8532 1128 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.0it/s 1.3s0.2s\n",
" all 548 35895 0.693 0.528 0.576 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 130/300 21.3G 0.9956 0.5035 0.8513 678 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.2it/s 1.2s0.2s\n",
" all 548 35895 0.691 0.529 0.578 0.355\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 131/300 22.2G 0.9982 0.5033 0.8507 883 640: 100% ━━━━━━━━━━━━ 191/191 2.6it/s 1:120.3sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 6.7it/s 1.3s0.2s\n",
" all 548 35895 0.694 0.527 0.577 0.354\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
"\u001b[K 132/300 18.4G 0.9902 0.4993 0.8521 736 640: 100% ━━━━━━━━━━━━ 191/191 2.7it/s 1:110.2sss\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 7.1it/s 1.3s0.2s\n",
" all 548 35895 0.696 0.526 0.576 0.354\n",
"\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 40 epochs. Best results observed at epoch 92, best model saved as best.pt.\n",
"To update EarlyStopping(patience=40) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
"\n",
"132 epochs completed in 2.655 hours.\n",
"Optimizer stripped from /home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/last.pt, 52.0MB\n",
"Optimizer stripped from /home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.pt, 52.0MB\n",
"\n",
"Validating /home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.pt...\n",
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"Model summary (fused): 92 layers, 25,842,655 parameters, 0 gradients, 78.7 GFLOPs\n",
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 9/9 2.6it/s 3.4s0.2s\n",
" all 548 35895 0.677 0.541 0.579 0.359\n",
" person 531 13969 0.658 0.476 0.532 0.233\n",
" car 517 16039 0.814 0.799 0.845 0.608\n",
" truck 266 750 0.581 0.384 0.419 0.274\n",
" bus 131 251 0.762 0.594 0.634 0.469\n",
" motor 485 4886 0.569 0.451 0.464 0.211\n",
"Speed: 0.1ms preprocess, 2.3ms inference, 0.0ms loss, 0.8ms postprocess per image\n",
"Results saved to \u001b[1m/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class\u001b[0m\n"
]
}
],
"source": [
"# Load a pretrained YOLO11n model\n",
"model = YOLO('yolov8m.pt')\n",
"\n",
"train_results = model.train(\n",
" data=\"/home/cuuva/experiment/vis5class_exp/vis5class.yaml\", #['person','car', 'truck', 'bus', 'motor']\n",
" epochs=300,\n",
" imgsz=640,\n",
" batch=-1,\n",
" device=\"cuda\",\n",
" optimizer = 'AdamW',\n",
" lr0 = 0.001,\n",
" patience = 40,\n",
" project = 'vis5class_v8m',\n",
" name = '5class',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "681c71ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
"Model summary (fused): 92 layers, 25,842,655 parameters, 0 gradients, 78.7 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 9, 8400) (49.6 MB)\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 0.7s, saved as '/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.onnx' (98.8 MB)\n",
"\n",
"Export complete (0.8s)\n",
"Results saved to \u001b[1m/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights\u001b[0m\n",
"Predict: yolo predict task=detect model=/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.onnx imgsz=640 \n",
"Validate: yolo val task=detect model=/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.onnx imgsz=640 data=/home/cuuva/experiment/vis5class_exp/vis5class.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"data": {
"text/plain": [
"'/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.onnx'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = YOLO(\"/home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class/weights/best.pt\")\n",
"model.export(format=\"onnx\", imgsz=640, device=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48b48641",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

106
vis5class_exp/vis5class_v8m/5class/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/vis5class_exp/vis5class.yaml
epochs: 300
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: vis5class_v8m
name: 5class
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/vis5class_exp/vis5class_v8m/5class

133
vis5class_exp/vis5class_v8m/5class/results.csv
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@ -0,0 +1,133 @@
epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,72.7568,1.44479,1.19038,0.9667,0.52566,0.31226,0.32582,0.1911,1.43647,1.18109,0.94877,0.0671728,0.000331588,0.000331588
2,146.528,1.40881,0.95622,0.95553,0.5356,0.35396,0.38618,0.22625,1.39214,0.92107,0.93017,0.0341706,0.000662727,0.000662727
3,220.972,1.38951,0.91262,0.94708,0.46726,0.35979,0.35628,0.20499,1.42798,0.92396,0.94258,0.00116619,0.000991666,0.000991666
4,295.999,1.37759,0.89274,0.94359,0.53905,0.40865,0.42472,0.24325,1.38982,0.87942,0.93583,0.0009901,0.0009901,0.0009901
5,369.108,1.34903,0.85488,0.93877,0.57967,0.40161,0.43275,0.25839,1.35015,0.90369,0.92444,0.0009868,0.0009868,0.0009868
6,441.547,1.31924,0.82247,0.93214,0.55561,0.41851,0.44732,0.261,1.33912,0.84953,0.92315,0.0009835,0.0009835,0.0009835
7,513.863,1.3099,0.81025,0.92703,0.57822,0.41739,0.44369,0.26042,1.37561,0.84721,0.93165,0.0009802,0.0009802,0.0009802
8,586.151,1.29848,0.79646,0.92496,0.57232,0.42032,0.45419,0.2672,1.32583,0.80563,0.9213,0.0009769,0.0009769,0.0009769
9,658.669,1.28968,0.7875,0.9216,0.62341,0.43253,0.47696,0.28393,1.30096,0.83514,0.90956,0.0009736,0.0009736,0.0009736
10,731.115,1.27546,0.77488,0.91999,0.6093,0.43106,0.47425,0.28075,1.31621,0.79459,0.9158,0.0009703,0.0009703,0.0009703
11,803.401,1.26559,0.75875,0.91754,0.61841,0.44194,0.48246,0.28928,1.29203,0.7826,0.90953,0.000967,0.000967,0.000967
12,875.713,1.25864,0.75592,0.91442,0.61122,0.44982,0.48905,0.29168,1.30185,0.78002,0.90787,0.0009637,0.0009637,0.0009637
13,948.224,1.2525,0.74269,0.91121,0.60124,0.4494,0.48587,0.28626,1.30836,0.7826,0.90989,0.0009604,0.0009604,0.0009604
14,1020.71,1.24633,0.74201,0.91098,0.60845,0.46694,0.49534,0.29667,1.29689,0.78034,0.91402,0.0009571,0.0009571,0.0009571
15,1093.09,1.24055,0.72816,0.90845,0.64826,0.4511,0.50353,0.30391,1.26609,0.76508,0.89919,0.0009538,0.0009538,0.0009538
16,1165.42,1.23457,0.72571,0.90812,0.62844,0.46435,0.5121,0.30498,1.27594,0.75764,0.90386,0.0009505,0.0009505,0.0009505
17,1237.76,1.22804,0.72059,0.90655,0.62564,0.47582,0.51764,0.31286,1.26774,0.74107,0.90222,0.0009472,0.0009472,0.0009472
18,1310.08,1.23115,0.71606,0.90609,0.62078,0.46647,0.50553,0.3059,1.27465,0.75678,0.90188,0.0009439,0.0009439,0.0009439
19,1382.39,1.21313,0.70619,0.90398,0.61229,0.48042,0.51823,0.31552,1.26788,0.7552,0.90245,0.0009406,0.0009406,0.0009406
20,1454.67,1.21403,0.70214,0.90304,0.62203,0.48408,0.51592,0.31217,1.25737,0.73259,0.90361,0.0009373,0.0009373,0.0009373
21,1527.03,1.21845,0.70646,0.90377,0.64118,0.47577,0.51886,0.31596,1.25554,0.748,0.89879,0.000934,0.000934,0.000934
22,1599.52,1.19854,0.69233,0.90007,0.62212,0.47112,0.51607,0.31454,1.25119,0.73104,0.90035,0.0009307,0.0009307,0.0009307
23,1671.84,1.19967,0.69096,0.90002,0.65247,0.47538,0.52464,0.31917,1.24608,0.72681,0.89989,0.0009274,0.0009274,0.0009274
24,1744.1,1.19214,0.67943,0.89803,0.64346,0.48673,0.5285,0.32305,1.24095,0.72563,0.89791,0.0009241,0.0009241,0.0009241
25,1816.5,1.19568,0.68199,0.89997,0.65985,0.495,0.5448,0.33145,1.24235,0.72473,0.89875,0.0009208,0.0009208,0.0009208
26,1888.78,1.18738,0.67711,0.89834,0.6301,0.48075,0.53157,0.32252,1.23859,0.73707,0.89891,0.0009175,0.0009175,0.0009175
27,1961.09,1.19036,0.67488,0.89663,0.65624,0.48764,0.54258,0.32831,1.23368,0.71317,0.89764,0.0009142,0.0009142,0.0009142
28,2033.4,1.19103,0.67453,0.89523,0.62319,0.50315,0.53056,0.32432,1.23825,0.72077,0.89874,0.0009109,0.0009109,0.0009109
29,2105.65,1.18587,0.67044,0.89428,0.67528,0.47607,0.53993,0.326,1.22415,0.71254,0.89454,0.0009076,0.0009076,0.0009076
30,2177.87,1.17865,0.66183,0.89554,0.67226,0.48936,0.54047,0.33084,1.22137,0.71192,0.89395,0.0009043,0.0009043,0.0009043
31,2250.1,1.17282,0.66441,0.89208,0.67328,0.4936,0.54521,0.33471,1.21474,0.70494,0.89327,0.000901,0.000901,0.000901
32,2322.44,1.1782,0.66251,0.89295,0.66042,0.49383,0.54527,0.33374,1.22493,0.70325,0.89339,0.0008977,0.0008977,0.0008977
33,2394.66,1.16764,0.65303,0.89151,0.67183,0.497,0.5482,0.33305,1.23193,0.69596,0.89748,0.0008944,0.0008944,0.0008944
34,2466.88,1.16184,0.64893,0.89027,0.65086,0.49235,0.53969,0.32907,1.21816,0.70487,0.89324,0.0008911,0.0008911,0.0008911
35,2539.16,1.15984,0.64608,0.88939,0.65227,0.49514,0.54504,0.33303,1.22438,0.69745,0.89576,0.0008878,0.0008878,0.0008878
36,2611.39,1.16221,0.64757,0.89028,0.66156,0.4865,0.53504,0.32798,1.22514,0.70801,0.89295,0.0008845,0.0008845,0.0008845
37,2683.66,1.1559,0.64241,0.88895,0.66886,0.50362,0.55028,0.33661,1.20028,0.69788,0.88951,0.0008812,0.0008812,0.0008812
38,2756.07,1.16184,0.64711,0.8897,0.66301,0.50888,0.55185,0.33725,1.21322,0.69086,0.89153,0.0008779,0.0008779,0.0008779
39,2828.43,1.15611,0.63988,0.88705,0.68708,0.48925,0.54485,0.33338,1.21991,0.70563,0.89266,0.0008746,0.0008746,0.0008746
40,2900.69,1.14893,0.63472,0.88685,0.679,0.49606,0.54707,0.33667,1.21558,0.69434,0.89155,0.0008713,0.0008713,0.0008713
41,2973.01,1.14496,0.62893,0.88481,0.67696,0.49122,0.55066,0.33988,1.21175,0.6891,0.8921,0.000868,0.000868,0.000868
42,3045.39,1.14668,0.63065,0.88718,0.66638,0.51362,0.55658,0.3427,1.21471,0.69933,0.89103,0.0008647,0.0008647,0.0008647
43,3117.82,1.14663,0.62913,0.88614,0.67651,0.51428,0.55969,0.34461,1.20413,0.69234,0.8909,0.0008614,0.0008614,0.0008614
44,3190.16,1.1391,0.62178,0.88394,0.66959,0.4986,0.55404,0.33902,1.21575,0.68684,0.89197,0.0008581,0.0008581,0.0008581
45,3262.53,1.14195,0.6234,0.88342,0.66141,0.50379,0.54965,0.33541,1.20795,0.69653,0.89144,0.0008548,0.0008548,0.0008548
46,3334.78,1.14212,0.62532,0.88499,0.6708,0.49769,0.55324,0.34147,1.20663,0.69257,0.88954,0.0008515,0.0008515,0.0008515
47,3407.25,1.13047,0.61754,0.88195,0.6788,0.49389,0.55125,0.33948,1.20174,0.68798,0.89172,0.0008482,0.0008482,0.0008482
48,3479.52,1.13584,0.6174,0.88417,0.67839,0.50833,0.56009,0.34431,1.207,0.68707,0.89112,0.0008449,0.0008449,0.0008449
49,3551.83,1.12563,0.61143,0.88157,0.6831,0.50507,0.55869,0.34467,1.21072,0.68338,0.89356,0.0008416,0.0008416,0.0008416
50,3624.15,1.1301,0.6113,0.88095,0.67488,0.50928,0.55984,0.34559,1.20584,0.68578,0.8908,0.0008383,0.0008383,0.0008383
51,3696.59,1.12386,0.60888,0.8796,0.66433,0.51488,0.56042,0.34534,1.2089,0.68237,0.89281,0.000835,0.000835,0.000835
52,3768.83,1.12375,0.6087,0.88108,0.68546,0.51061,0.56772,0.34676,1.20313,0.68138,0.88888,0.0008317,0.0008317,0.0008317
53,3841.24,1.12149,0.60466,0.8787,0.682,0.50907,0.56022,0.3427,1.20168,0.68234,0.89215,0.0008284,0.0008284,0.0008284
54,3913.56,1.11817,0.60223,0.87751,0.6822,0.51812,0.56755,0.34812,1.20187,0.67429,0.88937,0.0008251,0.0008251,0.0008251
55,3985.94,1.11051,0.59606,0.8784,0.69197,0.51877,0.57068,0.34954,1.19689,0.67102,0.88849,0.0008218,0.0008218,0.0008218
56,4058.24,1.11085,0.59297,0.87846,0.66953,0.51478,0.56063,0.34444,1.1969,0.6731,0.89011,0.0008185,0.0008185,0.0008185
57,4130.61,1.1046,0.5927,0.87597,0.65563,0.50094,0.55575,0.34249,1.19594,0.68285,0.88795,0.0008152,0.0008152,0.0008152
58,4202.88,1.10851,0.59355,0.87697,0.65894,0.51787,0.56094,0.34476,1.19487,0.68033,0.89029,0.0008119,0.0008119,0.0008119
59,4275.1,1.11166,0.59444,0.87689,0.66819,0.52964,0.56819,0.34948,1.18841,0.67189,0.88718,0.0008086,0.0008086,0.0008086
60,4347.3,1.11402,0.59459,0.87766,0.66618,0.52623,0.56653,0.35045,1.19258,0.67685,0.88582,0.0008053,0.0008053,0.0008053
61,4419.61,1.10603,0.59023,0.87597,0.67289,0.5269,0.56668,0.35173,1.19458,0.67252,0.88722,0.000802,0.000802,0.000802
62,4492.02,1.1089,0.58893,0.87539,0.69442,0.51666,0.57002,0.35218,1.19512,0.67205,0.88742,0.0007987,0.0007987,0.0007987
63,4564.35,1.10126,0.58544,0.8747,0.67927,0.51548,0.5658,0.34873,1.19728,0.67687,0.88857,0.0007954,0.0007954,0.0007954
64,4636.68,1.10015,0.58379,0.87463,0.69835,0.51765,0.57147,0.35158,1.1948,0.6743,0.89006,0.0007921,0.0007921,0.0007921
65,4708.95,1.09685,0.58521,0.87389,0.67123,0.52733,0.571,0.34998,1.20161,0.66962,0.89241,0.0007888,0.0007888,0.0007888
66,4781.15,1.09412,0.57933,0.87283,0.68204,0.51892,0.56677,0.35045,1.1912,0.66631,0.89006,0.0007855,0.0007855,0.0007855
67,4853.4,1.09323,0.57828,0.87218,0.68187,0.52194,0.57107,0.35096,1.19295,0.66392,0.89132,0.0007822,0.0007822,0.0007822
68,4925.57,1.09542,0.57942,0.87359,0.66349,0.52872,0.56969,0.35069,1.1977,0.67358,0.89008,0.0007789,0.0007789,0.0007789
69,4997.8,1.08848,0.57502,0.87253,0.68603,0.52252,0.5721,0.3527,1.19264,0.66167,0.89018,0.0007756,0.0007756,0.0007756
70,5070.07,1.0797,0.56895,0.87086,0.67564,0.52518,0.57448,0.3537,1.19413,0.67271,0.88964,0.0007723,0.0007723,0.0007723
71,5142.4,1.08952,0.57364,0.8729,0.67722,0.52944,0.5732,0.35254,1.1932,0.66443,0.8912,0.000769,0.000769,0.000769
72,5214.69,1.08708,0.5741,0.87103,0.69565,0.52059,0.57491,0.35386,1.19117,0.66422,0.89011,0.0007657,0.0007657,0.0007657
73,5287.15,1.07594,0.56612,0.86938,0.69976,0.51829,0.5745,0.35339,1.18939,0.66648,0.88708,0.0007624,0.0007624,0.0007624
74,5359.37,1.08267,0.5652,0.86979,0.68236,0.52436,0.5731,0.35212,1.19171,0.6614,0.88856,0.0007591,0.0007591,0.0007591
75,5431.66,1.07528,0.56457,0.86918,0.68625,0.52982,0.57569,0.35554,1.1933,0.65658,0.88714,0.0007558,0.0007558,0.0007558
76,5503.89,1.08461,0.56925,0.87057,0.70377,0.51155,0.572,0.35407,1.19292,0.66601,0.88734,0.0007525,0.0007525,0.0007525
77,5576.13,1.07366,0.56236,0.86966,0.68546,0.52834,0.57438,0.3551,1.1946,0.66593,0.88785,0.0007492,0.0007492,0.0007492
78,5648.34,1.07155,0.55735,0.86862,0.70273,0.5239,0.57413,0.35533,1.18901,0.66374,0.88651,0.0007459,0.0007459,0.0007459
79,5720.68,1.06394,0.55246,0.8673,0.68239,0.52979,0.57519,0.35688,1.19014,0.6592,0.88768,0.0007426,0.0007426,0.0007426
80,5792.96,1.07301,0.56063,0.86597,0.67787,0.53589,0.57642,0.35638,1.18833,0.66365,0.88654,0.0007393,0.0007393,0.0007393
81,5865.13,1.06688,0.55651,0.8677,0.70261,0.52034,0.5774,0.35753,1.18545,0.66171,0.88895,0.000736,0.000736,0.000736
82,5937.49,1.05782,0.55102,0.86717,0.69084,0.52252,0.57526,0.35372,1.18495,0.66243,0.88584,0.0007327,0.0007327,0.0007327
83,6009.86,1.06942,0.55865,0.86761,0.67928,0.53153,0.57923,0.35659,1.18692,0.65926,0.88678,0.0007294,0.0007294,0.0007294
84,6082.16,1.06156,0.55052,0.86748,0.69249,0.52683,0.57718,0.3554,1.18747,0.66281,0.88825,0.0007261,0.0007261,0.0007261
85,6154.46,1.06163,0.5504,0.86661,0.70651,0.51678,0.57279,0.35238,1.18784,0.66091,0.88734,0.0007228,0.0007228,0.0007228
86,6226.73,1.05978,0.55075,0.86498,0.69302,0.52674,0.57401,0.3535,1.18608,0.66251,0.88808,0.0007195,0.0007195,0.0007195
87,6298.97,1.06763,0.55596,0.86671,0.69507,0.52814,0.57564,0.35602,1.18863,0.6619,0.88688,0.0007162,0.0007162,0.0007162
88,6371.21,1.05527,0.54906,0.86485,0.68936,0.53185,0.57534,0.3559,1.18632,0.66433,0.88541,0.0007129,0.0007129,0.0007129
89,6443.46,1.05816,0.54908,0.86506,0.68943,0.52236,0.57442,0.35652,1.18542,0.66013,0.88579,0.0007096,0.0007096,0.0007096
90,6515.77,1.0599,0.54574,0.86374,0.68979,0.52658,0.5734,0.35609,1.1843,0.65965,0.88657,0.0007063,0.0007063,0.0007063
91,6588.09,1.05442,0.54542,0.86247,0.69883,0.52824,0.57886,0.35701,1.18908,0.66049,0.8874,0.000703,0.000703,0.000703
92,6660.36,1.05574,0.54564,0.86405,0.67881,0.54114,0.57866,0.35908,1.1866,0.65861,0.88596,0.0006997,0.0006997,0.0006997
93,6732.82,1.05693,0.54519,0.86332,0.68776,0.53119,0.57482,0.35621,1.18658,0.6616,0.88643,0.0006964,0.0006964,0.0006964
94,6805.04,1.04346,0.53688,0.86144,0.69365,0.53376,0.5761,0.35619,1.18791,0.66342,0.88622,0.0006931,0.0006931,0.0006931
95,6877.29,1.03656,0.53459,0.86088,0.69556,0.53029,0.57856,0.35784,1.19223,0.66052,0.88895,0.0006898,0.0006898,0.0006898
96,6949.37,1.04681,0.53917,0.86335,0.69806,0.53114,0.58105,0.35808,1.19037,0.65811,0.88828,0.0006865,0.0006865,0.0006865
97,7021.67,1.04488,0.53766,0.86197,0.68827,0.5324,0.57903,0.35806,1.18978,0.65673,0.88708,0.0006832,0.0006832,0.0006832
98,7093.91,1.04218,0.53513,0.8637,0.69459,0.53054,0.57667,0.35815,1.18858,0.65629,0.88636,0.0006799,0.0006799,0.0006799
99,7166.2,1.03995,0.53279,0.86021,0.69154,0.53212,0.57719,0.35785,1.1884,0.65631,0.88687,0.0006766,0.0006766,0.0006766
100,7238.52,1.03859,0.53426,0.86097,0.68992,0.52582,0.5771,0.35559,1.19059,0.65758,0.88754,0.0006733,0.0006733,0.0006733
101,7310.87,1.03222,0.5306,0.86112,0.69893,0.52928,0.57677,0.3558,1.19042,0.65712,0.88819,0.00067,0.00067,0.00067
102,7383.01,1.037,0.53206,0.86088,0.69906,0.52487,0.57772,0.35539,1.18846,0.65588,0.8884,0.0006667,0.0006667,0.0006667
103,7455.24,1.0281,0.52749,0.85875,0.6991,0.52327,0.57584,0.35528,1.18785,0.65254,0.8885,0.0006634,0.0006634,0.0006634
104,7527.47,1.03388,0.52716,0.86047,0.69553,0.52439,0.57501,0.35434,1.18834,0.65659,0.88853,0.0006601,0.0006601,0.0006601
105,7599.69,1.03391,0.52851,0.85969,0.68635,0.53102,0.57379,0.35429,1.18981,0.66117,0.88868,0.0006568,0.0006568,0.0006568
106,7671.97,1.03233,0.52718,0.85894,0.697,0.52916,0.57698,0.355,1.1898,0.66,0.88799,0.0006535,0.0006535,0.0006535
107,7744.21,1.02989,0.52766,0.86019,0.68845,0.5312,0.57663,0.35424,1.19084,0.65885,0.88822,0.0006502,0.0006502,0.0006502
108,7816.41,1.03038,0.52719,0.8587,0.69494,0.52434,0.57476,0.3537,1.19158,0.65954,0.88774,0.0006469,0.0006469,0.0006469
109,7888.67,1.02772,0.5258,0.85871,0.6863,0.52964,0.57246,0.35286,1.19185,0.66012,0.88833,0.0006436,0.0006436,0.0006436
110,7960.92,1.01932,0.51972,0.85635,0.68106,0.53027,0.57405,0.35411,1.18992,0.66047,0.88792,0.0006403,0.0006403,0.0006403
111,8033.14,1.03017,0.5242,0.85706,0.6738,0.53844,0.57559,0.35547,1.18852,0.66085,0.8874,0.000637,0.000637,0.000637
112,8105.43,1.01654,0.51729,0.85625,0.67623,0.53686,0.57646,0.3551,1.18867,0.65712,0.88721,0.0006337,0.0006337,0.0006337
113,8177.78,1.01925,0.51799,0.8554,0.68948,0.52755,0.57733,0.35621,1.1897,0.65658,0.8878,0.0006304,0.0006304,0.0006304
114,8250.07,1.01855,0.51732,0.85675,0.69542,0.52984,0.57751,0.35644,1.19212,0.65599,0.88826,0.0006271,0.0006271,0.0006271
115,8322.49,1.01451,0.51586,0.85473,0.68239,0.53452,0.57679,0.35638,1.19069,0.65772,0.88853,0.0006238,0.0006238,0.0006238
116,8394.67,1.00699,0.51249,0.8559,0.69969,0.5265,0.57752,0.35535,1.19107,0.6581,0.88906,0.0006205,0.0006205,0.0006205
117,8466.95,1.0094,0.51229,0.85435,0.70182,0.52175,0.57716,0.35571,1.1901,0.65652,0.8898,0.0006172,0.0006172,0.0006172
118,8539.22,1.01758,0.51645,0.85537,0.68603,0.52839,0.57753,0.35669,1.19028,0.65602,0.8903,0.0006139,0.0006139,0.0006139
119,8611.47,1.00895,0.51196,0.85482,0.68942,0.53026,0.57957,0.35736,1.19022,0.65459,0.89051,0.0006106,0.0006106,0.0006106
120,8683.69,1.00709,0.51166,0.85558,0.68847,0.53302,0.57853,0.35673,1.19074,0.65581,0.8904,0.0006073,0.0006073,0.0006073
121,8756.02,1.00848,0.50997,0.85363,0.69659,0.52931,0.57847,0.35762,1.19145,0.65595,0.89088,0.000604,0.000604,0.000604
122,8828.23,1.00919,0.51241,0.85425,0.70043,0.52817,0.57946,0.35727,1.19161,0.65648,0.89055,0.0006007,0.0006007,0.0006007
123,8900.5,1.00918,0.51109,0.85328,0.69719,0.52818,0.58011,0.35753,1.19103,0.65598,0.89025,0.0005974,0.0005974,0.0005974
124,8974.49,1.00047,0.5067,0.85325,0.7024,0.5209,0.57788,0.35653,1.19385,0.65601,0.891,0.0005941,0.0005941,0.0005941
125,9048.16,1.00401,0.50905,0.85464,0.69441,0.52487,0.57741,0.35698,1.19312,0.65498,0.89075,0.0005908,0.0005908,0.0005908
126,9122.38,1.00738,0.50857,0.85508,0.68938,0.53035,0.57848,0.35667,1.19199,0.65598,0.89048,0.0005875,0.0005875,0.0005875
127,9194.96,0.99834,0.50425,0.85134,0.68983,0.52851,0.57842,0.3559,1.19197,0.6546,0.89047,0.0005842,0.0005842,0.0005842
128,9267.25,0.99429,0.50238,0.85247,0.70076,0.52259,0.57722,0.35562,1.19217,0.65469,0.89068,0.0005809,0.0005809,0.0005809
129,9339.76,1.00197,0.50593,0.85321,0.69296,0.52789,0.57646,0.35483,1.19223,0.65465,0.89073,0.0005776,0.0005776,0.0005776
130,9412.1,0.99559,0.50348,0.85127,0.6909,0.52884,0.57788,0.35486,1.19113,0.65186,0.89076,0.0005743,0.0005743,0.0005743
131,9486.26,0.99815,0.50326,0.85075,0.69389,0.52696,0.57719,0.35445,1.19055,0.65211,0.89079,0.000571,0.000571,0.000571
132,9558.8,0.9902,0.49932,0.85206,0.69645,0.5264,0.57649,0.35434,1.19113,0.65155,0.8908,0.0005677,0.0005677,0.0005677
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 72.7568 1.44479 1.19038 0.9667 0.52566 0.31226 0.32582 0.1911 1.43647 1.18109 0.94877 0.0671728 0.000331588 0.000331588
3 2 146.528 1.40881 0.95622 0.95553 0.5356 0.35396 0.38618 0.22625 1.39214 0.92107 0.93017 0.0341706 0.000662727 0.000662727
4 3 220.972 1.38951 0.91262 0.94708 0.46726 0.35979 0.35628 0.20499 1.42798 0.92396 0.94258 0.00116619 0.000991666 0.000991666
5 4 295.999 1.37759 0.89274 0.94359 0.53905 0.40865 0.42472 0.24325 1.38982 0.87942 0.93583 0.0009901 0.0009901 0.0009901
6 5 369.108 1.34903 0.85488 0.93877 0.57967 0.40161 0.43275 0.25839 1.35015 0.90369 0.92444 0.0009868 0.0009868 0.0009868
7 6 441.547 1.31924 0.82247 0.93214 0.55561 0.41851 0.44732 0.261 1.33912 0.84953 0.92315 0.0009835 0.0009835 0.0009835
8 7 513.863 1.3099 0.81025 0.92703 0.57822 0.41739 0.44369 0.26042 1.37561 0.84721 0.93165 0.0009802 0.0009802 0.0009802
9 8 586.151 1.29848 0.79646 0.92496 0.57232 0.42032 0.45419 0.2672 1.32583 0.80563 0.9213 0.0009769 0.0009769 0.0009769
10 9 658.669 1.28968 0.7875 0.9216 0.62341 0.43253 0.47696 0.28393 1.30096 0.83514 0.90956 0.0009736 0.0009736 0.0009736
11 10 731.115 1.27546 0.77488 0.91999 0.6093 0.43106 0.47425 0.28075 1.31621 0.79459 0.9158 0.0009703 0.0009703 0.0009703
12 11 803.401 1.26559 0.75875 0.91754 0.61841 0.44194 0.48246 0.28928 1.29203 0.7826 0.90953 0.000967 0.000967 0.000967
13 12 875.713 1.25864 0.75592 0.91442 0.61122 0.44982 0.48905 0.29168 1.30185 0.78002 0.90787 0.0009637 0.0009637 0.0009637
14 13 948.224 1.2525 0.74269 0.91121 0.60124 0.4494 0.48587 0.28626 1.30836 0.7826 0.90989 0.0009604 0.0009604 0.0009604
15 14 1020.71 1.24633 0.74201 0.91098 0.60845 0.46694 0.49534 0.29667 1.29689 0.78034 0.91402 0.0009571 0.0009571 0.0009571
16 15 1093.09 1.24055 0.72816 0.90845 0.64826 0.4511 0.50353 0.30391 1.26609 0.76508 0.89919 0.0009538 0.0009538 0.0009538
17 16 1165.42 1.23457 0.72571 0.90812 0.62844 0.46435 0.5121 0.30498 1.27594 0.75764 0.90386 0.0009505 0.0009505 0.0009505
18 17 1237.76 1.22804 0.72059 0.90655 0.62564 0.47582 0.51764 0.31286 1.26774 0.74107 0.90222 0.0009472 0.0009472 0.0009472
19 18 1310.08 1.23115 0.71606 0.90609 0.62078 0.46647 0.50553 0.3059 1.27465 0.75678 0.90188 0.0009439 0.0009439 0.0009439
20 19 1382.39 1.21313 0.70619 0.90398 0.61229 0.48042 0.51823 0.31552 1.26788 0.7552 0.90245 0.0009406 0.0009406 0.0009406
21 20 1454.67 1.21403 0.70214 0.90304 0.62203 0.48408 0.51592 0.31217 1.25737 0.73259 0.90361 0.0009373 0.0009373 0.0009373
22 21 1527.03 1.21845 0.70646 0.90377 0.64118 0.47577 0.51886 0.31596 1.25554 0.748 0.89879 0.000934 0.000934 0.000934
23 22 1599.52 1.19854 0.69233 0.90007 0.62212 0.47112 0.51607 0.31454 1.25119 0.73104 0.90035 0.0009307 0.0009307 0.0009307
24 23 1671.84 1.19967 0.69096 0.90002 0.65247 0.47538 0.52464 0.31917 1.24608 0.72681 0.89989 0.0009274 0.0009274 0.0009274
25 24 1744.1 1.19214 0.67943 0.89803 0.64346 0.48673 0.5285 0.32305 1.24095 0.72563 0.89791 0.0009241 0.0009241 0.0009241
26 25 1816.5 1.19568 0.68199 0.89997 0.65985 0.495 0.5448 0.33145 1.24235 0.72473 0.89875 0.0009208 0.0009208 0.0009208
27 26 1888.78 1.18738 0.67711 0.89834 0.6301 0.48075 0.53157 0.32252 1.23859 0.73707 0.89891 0.0009175 0.0009175 0.0009175
28 27 1961.09 1.19036 0.67488 0.89663 0.65624 0.48764 0.54258 0.32831 1.23368 0.71317 0.89764 0.0009142 0.0009142 0.0009142
29 28 2033.4 1.19103 0.67453 0.89523 0.62319 0.50315 0.53056 0.32432 1.23825 0.72077 0.89874 0.0009109 0.0009109 0.0009109
30 29 2105.65 1.18587 0.67044 0.89428 0.67528 0.47607 0.53993 0.326 1.22415 0.71254 0.89454 0.0009076 0.0009076 0.0009076
31 30 2177.87 1.17865 0.66183 0.89554 0.67226 0.48936 0.54047 0.33084 1.22137 0.71192 0.89395 0.0009043 0.0009043 0.0009043
32 31 2250.1 1.17282 0.66441 0.89208 0.67328 0.4936 0.54521 0.33471 1.21474 0.70494 0.89327 0.000901 0.000901 0.000901
33 32 2322.44 1.1782 0.66251 0.89295 0.66042 0.49383 0.54527 0.33374 1.22493 0.70325 0.89339 0.0008977 0.0008977 0.0008977
34 33 2394.66 1.16764 0.65303 0.89151 0.67183 0.497 0.5482 0.33305 1.23193 0.69596 0.89748 0.0008944 0.0008944 0.0008944
35 34 2466.88 1.16184 0.64893 0.89027 0.65086 0.49235 0.53969 0.32907 1.21816 0.70487 0.89324 0.0008911 0.0008911 0.0008911
36 35 2539.16 1.15984 0.64608 0.88939 0.65227 0.49514 0.54504 0.33303 1.22438 0.69745 0.89576 0.0008878 0.0008878 0.0008878
37 36 2611.39 1.16221 0.64757 0.89028 0.66156 0.4865 0.53504 0.32798 1.22514 0.70801 0.89295 0.0008845 0.0008845 0.0008845
38 37 2683.66 1.1559 0.64241 0.88895 0.66886 0.50362 0.55028 0.33661 1.20028 0.69788 0.88951 0.0008812 0.0008812 0.0008812
39 38 2756.07 1.16184 0.64711 0.8897 0.66301 0.50888 0.55185 0.33725 1.21322 0.69086 0.89153 0.0008779 0.0008779 0.0008779
40 39 2828.43 1.15611 0.63988 0.88705 0.68708 0.48925 0.54485 0.33338 1.21991 0.70563 0.89266 0.0008746 0.0008746 0.0008746
41 40 2900.69 1.14893 0.63472 0.88685 0.679 0.49606 0.54707 0.33667 1.21558 0.69434 0.89155 0.0008713 0.0008713 0.0008713
42 41 2973.01 1.14496 0.62893 0.88481 0.67696 0.49122 0.55066 0.33988 1.21175 0.6891 0.8921 0.000868 0.000868 0.000868
43 42 3045.39 1.14668 0.63065 0.88718 0.66638 0.51362 0.55658 0.3427 1.21471 0.69933 0.89103 0.0008647 0.0008647 0.0008647
44 43 3117.82 1.14663 0.62913 0.88614 0.67651 0.51428 0.55969 0.34461 1.20413 0.69234 0.8909 0.0008614 0.0008614 0.0008614
45 44 3190.16 1.1391 0.62178 0.88394 0.66959 0.4986 0.55404 0.33902 1.21575 0.68684 0.89197 0.0008581 0.0008581 0.0008581
46 45 3262.53 1.14195 0.6234 0.88342 0.66141 0.50379 0.54965 0.33541 1.20795 0.69653 0.89144 0.0008548 0.0008548 0.0008548
47 46 3334.78 1.14212 0.62532 0.88499 0.6708 0.49769 0.55324 0.34147 1.20663 0.69257 0.88954 0.0008515 0.0008515 0.0008515
48 47 3407.25 1.13047 0.61754 0.88195 0.6788 0.49389 0.55125 0.33948 1.20174 0.68798 0.89172 0.0008482 0.0008482 0.0008482
49 48 3479.52 1.13584 0.6174 0.88417 0.67839 0.50833 0.56009 0.34431 1.207 0.68707 0.89112 0.0008449 0.0008449 0.0008449
50 49 3551.83 1.12563 0.61143 0.88157 0.6831 0.50507 0.55869 0.34467 1.21072 0.68338 0.89356 0.0008416 0.0008416 0.0008416
51 50 3624.15 1.1301 0.6113 0.88095 0.67488 0.50928 0.55984 0.34559 1.20584 0.68578 0.8908 0.0008383 0.0008383 0.0008383
52 51 3696.59 1.12386 0.60888 0.8796 0.66433 0.51488 0.56042 0.34534 1.2089 0.68237 0.89281 0.000835 0.000835 0.000835
53 52 3768.83 1.12375 0.6087 0.88108 0.68546 0.51061 0.56772 0.34676 1.20313 0.68138 0.88888 0.0008317 0.0008317 0.0008317
54 53 3841.24 1.12149 0.60466 0.8787 0.682 0.50907 0.56022 0.3427 1.20168 0.68234 0.89215 0.0008284 0.0008284 0.0008284
55 54 3913.56 1.11817 0.60223 0.87751 0.6822 0.51812 0.56755 0.34812 1.20187 0.67429 0.88937 0.0008251 0.0008251 0.0008251
56 55 3985.94 1.11051 0.59606 0.8784 0.69197 0.51877 0.57068 0.34954 1.19689 0.67102 0.88849 0.0008218 0.0008218 0.0008218
57 56 4058.24 1.11085 0.59297 0.87846 0.66953 0.51478 0.56063 0.34444 1.1969 0.6731 0.89011 0.0008185 0.0008185 0.0008185
58 57 4130.61 1.1046 0.5927 0.87597 0.65563 0.50094 0.55575 0.34249 1.19594 0.68285 0.88795 0.0008152 0.0008152 0.0008152
59 58 4202.88 1.10851 0.59355 0.87697 0.65894 0.51787 0.56094 0.34476 1.19487 0.68033 0.89029 0.0008119 0.0008119 0.0008119
60 59 4275.1 1.11166 0.59444 0.87689 0.66819 0.52964 0.56819 0.34948 1.18841 0.67189 0.88718 0.0008086 0.0008086 0.0008086
61 60 4347.3 1.11402 0.59459 0.87766 0.66618 0.52623 0.56653 0.35045 1.19258 0.67685 0.88582 0.0008053 0.0008053 0.0008053
62 61 4419.61 1.10603 0.59023 0.87597 0.67289 0.5269 0.56668 0.35173 1.19458 0.67252 0.88722 0.000802 0.000802 0.000802
63 62 4492.02 1.1089 0.58893 0.87539 0.69442 0.51666 0.57002 0.35218 1.19512 0.67205 0.88742 0.0007987 0.0007987 0.0007987
64 63 4564.35 1.10126 0.58544 0.8747 0.67927 0.51548 0.5658 0.34873 1.19728 0.67687 0.88857 0.0007954 0.0007954 0.0007954
65 64 4636.68 1.10015 0.58379 0.87463 0.69835 0.51765 0.57147 0.35158 1.1948 0.6743 0.89006 0.0007921 0.0007921 0.0007921
66 65 4708.95 1.09685 0.58521 0.87389 0.67123 0.52733 0.571 0.34998 1.20161 0.66962 0.89241 0.0007888 0.0007888 0.0007888
67 66 4781.15 1.09412 0.57933 0.87283 0.68204 0.51892 0.56677 0.35045 1.1912 0.66631 0.89006 0.0007855 0.0007855 0.0007855
68 67 4853.4 1.09323 0.57828 0.87218 0.68187 0.52194 0.57107 0.35096 1.19295 0.66392 0.89132 0.0007822 0.0007822 0.0007822
69 68 4925.57 1.09542 0.57942 0.87359 0.66349 0.52872 0.56969 0.35069 1.1977 0.67358 0.89008 0.0007789 0.0007789 0.0007789
70 69 4997.8 1.08848 0.57502 0.87253 0.68603 0.52252 0.5721 0.3527 1.19264 0.66167 0.89018 0.0007756 0.0007756 0.0007756
71 70 5070.07 1.0797 0.56895 0.87086 0.67564 0.52518 0.57448 0.3537 1.19413 0.67271 0.88964 0.0007723 0.0007723 0.0007723
72 71 5142.4 1.08952 0.57364 0.8729 0.67722 0.52944 0.5732 0.35254 1.1932 0.66443 0.8912 0.000769 0.000769 0.000769
73 72 5214.69 1.08708 0.5741 0.87103 0.69565 0.52059 0.57491 0.35386 1.19117 0.66422 0.89011 0.0007657 0.0007657 0.0007657
74 73 5287.15 1.07594 0.56612 0.86938 0.69976 0.51829 0.5745 0.35339 1.18939 0.66648 0.88708 0.0007624 0.0007624 0.0007624
75 74 5359.37 1.08267 0.5652 0.86979 0.68236 0.52436 0.5731 0.35212 1.19171 0.6614 0.88856 0.0007591 0.0007591 0.0007591
76 75 5431.66 1.07528 0.56457 0.86918 0.68625 0.52982 0.57569 0.35554 1.1933 0.65658 0.88714 0.0007558 0.0007558 0.0007558
77 76 5503.89 1.08461 0.56925 0.87057 0.70377 0.51155 0.572 0.35407 1.19292 0.66601 0.88734 0.0007525 0.0007525 0.0007525
78 77 5576.13 1.07366 0.56236 0.86966 0.68546 0.52834 0.57438 0.3551 1.1946 0.66593 0.88785 0.0007492 0.0007492 0.0007492
79 78 5648.34 1.07155 0.55735 0.86862 0.70273 0.5239 0.57413 0.35533 1.18901 0.66374 0.88651 0.0007459 0.0007459 0.0007459
80 79 5720.68 1.06394 0.55246 0.8673 0.68239 0.52979 0.57519 0.35688 1.19014 0.6592 0.88768 0.0007426 0.0007426 0.0007426
81 80 5792.96 1.07301 0.56063 0.86597 0.67787 0.53589 0.57642 0.35638 1.18833 0.66365 0.88654 0.0007393 0.0007393 0.0007393
82 81 5865.13 1.06688 0.55651 0.8677 0.70261 0.52034 0.5774 0.35753 1.18545 0.66171 0.88895 0.000736 0.000736 0.000736
83 82 5937.49 1.05782 0.55102 0.86717 0.69084 0.52252 0.57526 0.35372 1.18495 0.66243 0.88584 0.0007327 0.0007327 0.0007327
84 83 6009.86 1.06942 0.55865 0.86761 0.67928 0.53153 0.57923 0.35659 1.18692 0.65926 0.88678 0.0007294 0.0007294 0.0007294
85 84 6082.16 1.06156 0.55052 0.86748 0.69249 0.52683 0.57718 0.3554 1.18747 0.66281 0.88825 0.0007261 0.0007261 0.0007261
86 85 6154.46 1.06163 0.5504 0.86661 0.70651 0.51678 0.57279 0.35238 1.18784 0.66091 0.88734 0.0007228 0.0007228 0.0007228
87 86 6226.73 1.05978 0.55075 0.86498 0.69302 0.52674 0.57401 0.3535 1.18608 0.66251 0.88808 0.0007195 0.0007195 0.0007195
88 87 6298.97 1.06763 0.55596 0.86671 0.69507 0.52814 0.57564 0.35602 1.18863 0.6619 0.88688 0.0007162 0.0007162 0.0007162
89 88 6371.21 1.05527 0.54906 0.86485 0.68936 0.53185 0.57534 0.3559 1.18632 0.66433 0.88541 0.0007129 0.0007129 0.0007129
90 89 6443.46 1.05816 0.54908 0.86506 0.68943 0.52236 0.57442 0.35652 1.18542 0.66013 0.88579 0.0007096 0.0007096 0.0007096
91 90 6515.77 1.0599 0.54574 0.86374 0.68979 0.52658 0.5734 0.35609 1.1843 0.65965 0.88657 0.0007063 0.0007063 0.0007063
92 91 6588.09 1.05442 0.54542 0.86247 0.69883 0.52824 0.57886 0.35701 1.18908 0.66049 0.8874 0.000703 0.000703 0.000703
93 92 6660.36 1.05574 0.54564 0.86405 0.67881 0.54114 0.57866 0.35908 1.1866 0.65861 0.88596 0.0006997 0.0006997 0.0006997
94 93 6732.82 1.05693 0.54519 0.86332 0.68776 0.53119 0.57482 0.35621 1.18658 0.6616 0.88643 0.0006964 0.0006964 0.0006964
95 94 6805.04 1.04346 0.53688 0.86144 0.69365 0.53376 0.5761 0.35619 1.18791 0.66342 0.88622 0.0006931 0.0006931 0.0006931
96 95 6877.29 1.03656 0.53459 0.86088 0.69556 0.53029 0.57856 0.35784 1.19223 0.66052 0.88895 0.0006898 0.0006898 0.0006898
97 96 6949.37 1.04681 0.53917 0.86335 0.69806 0.53114 0.58105 0.35808 1.19037 0.65811 0.88828 0.0006865 0.0006865 0.0006865
98 97 7021.67 1.04488 0.53766 0.86197 0.68827 0.5324 0.57903 0.35806 1.18978 0.65673 0.88708 0.0006832 0.0006832 0.0006832
99 98 7093.91 1.04218 0.53513 0.8637 0.69459 0.53054 0.57667 0.35815 1.18858 0.65629 0.88636 0.0006799 0.0006799 0.0006799
100 99 7166.2 1.03995 0.53279 0.86021 0.69154 0.53212 0.57719 0.35785 1.1884 0.65631 0.88687 0.0006766 0.0006766 0.0006766
101 100 7238.52 1.03859 0.53426 0.86097 0.68992 0.52582 0.5771 0.35559 1.19059 0.65758 0.88754 0.0006733 0.0006733 0.0006733
102 101 7310.87 1.03222 0.5306 0.86112 0.69893 0.52928 0.57677 0.3558 1.19042 0.65712 0.88819 0.00067 0.00067 0.00067
103 102 7383.01 1.037 0.53206 0.86088 0.69906 0.52487 0.57772 0.35539 1.18846 0.65588 0.8884 0.0006667 0.0006667 0.0006667
104 103 7455.24 1.0281 0.52749 0.85875 0.6991 0.52327 0.57584 0.35528 1.18785 0.65254 0.8885 0.0006634 0.0006634 0.0006634
105 104 7527.47 1.03388 0.52716 0.86047 0.69553 0.52439 0.57501 0.35434 1.18834 0.65659 0.88853 0.0006601 0.0006601 0.0006601
106 105 7599.69 1.03391 0.52851 0.85969 0.68635 0.53102 0.57379 0.35429 1.18981 0.66117 0.88868 0.0006568 0.0006568 0.0006568
107 106 7671.97 1.03233 0.52718 0.85894 0.697 0.52916 0.57698 0.355 1.1898 0.66 0.88799 0.0006535 0.0006535 0.0006535
108 107 7744.21 1.02989 0.52766 0.86019 0.68845 0.5312 0.57663 0.35424 1.19084 0.65885 0.88822 0.0006502 0.0006502 0.0006502
109 108 7816.41 1.03038 0.52719 0.8587 0.69494 0.52434 0.57476 0.3537 1.19158 0.65954 0.88774 0.0006469 0.0006469 0.0006469
110 109 7888.67 1.02772 0.5258 0.85871 0.6863 0.52964 0.57246 0.35286 1.19185 0.66012 0.88833 0.0006436 0.0006436 0.0006436
111 110 7960.92 1.01932 0.51972 0.85635 0.68106 0.53027 0.57405 0.35411 1.18992 0.66047 0.88792 0.0006403 0.0006403 0.0006403
112 111 8033.14 1.03017 0.5242 0.85706 0.6738 0.53844 0.57559 0.35547 1.18852 0.66085 0.8874 0.000637 0.000637 0.000637
113 112 8105.43 1.01654 0.51729 0.85625 0.67623 0.53686 0.57646 0.3551 1.18867 0.65712 0.88721 0.0006337 0.0006337 0.0006337
114 113 8177.78 1.01925 0.51799 0.8554 0.68948 0.52755 0.57733 0.35621 1.1897 0.65658 0.8878 0.0006304 0.0006304 0.0006304
115 114 8250.07 1.01855 0.51732 0.85675 0.69542 0.52984 0.57751 0.35644 1.19212 0.65599 0.88826 0.0006271 0.0006271 0.0006271
116 115 8322.49 1.01451 0.51586 0.85473 0.68239 0.53452 0.57679 0.35638 1.19069 0.65772 0.88853 0.0006238 0.0006238 0.0006238
117 116 8394.67 1.00699 0.51249 0.8559 0.69969 0.5265 0.57752 0.35535 1.19107 0.6581 0.88906 0.0006205 0.0006205 0.0006205
118 117 8466.95 1.0094 0.51229 0.85435 0.70182 0.52175 0.57716 0.35571 1.1901 0.65652 0.8898 0.0006172 0.0006172 0.0006172
119 118 8539.22 1.01758 0.51645 0.85537 0.68603 0.52839 0.57753 0.35669 1.19028 0.65602 0.8903 0.0006139 0.0006139 0.0006139
120 119 8611.47 1.00895 0.51196 0.85482 0.68942 0.53026 0.57957 0.35736 1.19022 0.65459 0.89051 0.0006106 0.0006106 0.0006106
121 120 8683.69 1.00709 0.51166 0.85558 0.68847 0.53302 0.57853 0.35673 1.19074 0.65581 0.8904 0.0006073 0.0006073 0.0006073
122 121 8756.02 1.00848 0.50997 0.85363 0.69659 0.52931 0.57847 0.35762 1.19145 0.65595 0.89088 0.000604 0.000604 0.000604
123 122 8828.23 1.00919 0.51241 0.85425 0.70043 0.52817 0.57946 0.35727 1.19161 0.65648 0.89055 0.0006007 0.0006007 0.0006007
124 123 8900.5 1.00918 0.51109 0.85328 0.69719 0.52818 0.58011 0.35753 1.19103 0.65598 0.89025 0.0005974 0.0005974 0.0005974
125 124 8974.49 1.00047 0.5067 0.85325 0.7024 0.5209 0.57788 0.35653 1.19385 0.65601 0.891 0.0005941 0.0005941 0.0005941
126 125 9048.16 1.00401 0.50905 0.85464 0.69441 0.52487 0.57741 0.35698 1.19312 0.65498 0.89075 0.0005908 0.0005908 0.0005908
127 126 9122.38 1.00738 0.50857 0.85508 0.68938 0.53035 0.57848 0.35667 1.19199 0.65598 0.89048 0.0005875 0.0005875 0.0005875
128 127 9194.96 0.99834 0.50425 0.85134 0.68983 0.52851 0.57842 0.3559 1.19197 0.6546 0.89047 0.0005842 0.0005842 0.0005842
129 128 9267.25 0.99429 0.50238 0.85247 0.70076 0.52259 0.57722 0.35562 1.19217 0.65469 0.89068 0.0005809 0.0005809 0.0005809
130 129 9339.76 1.00197 0.50593 0.85321 0.69296 0.52789 0.57646 0.35483 1.19223 0.65465 0.89073 0.0005776 0.0005776 0.0005776
131 130 9412.1 0.99559 0.50348 0.85127 0.6909 0.52884 0.57788 0.35486 1.19113 0.65186 0.89076 0.0005743 0.0005743 0.0005743
132 131 9486.26 0.99815 0.50326 0.85075 0.69389 0.52696 0.57719 0.35445 1.19055 0.65211 0.89079 0.000571 0.000571 0.000571
133 132 9558.8 0.9902 0.49932 0.85206 0.69645 0.5264 0.57649 0.35434 1.19113 0.65155 0.8908 0.0005677 0.0005677 0.0005677

348
vis6class_exp/test.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "817586c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"🔍 Checking split: train (files: 6471)\n",
"\n",
"🔍 Checking split: val (files: 548)\n",
"\n",
"🔍 Checking split: test (files: 1610)\n",
"\n",
"📌 클래스 통계 결과:\n",
" - Class 0: 147747 개\n",
" - Class 1: 187005 개\n",
" - Class 2: 32702 개\n",
" - Class 3: 16284 개\n",
" - Class 4: 9117 개\n",
" - Class 5: 40378 개\n",
"\n",
"총 클래스 종류: 6\n"
]
}
],
"source": [
"import os\n",
"from collections import Counter\n",
"\n",
"# label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels copy\"\n",
"label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels\"\n",
"splits = [\"train\", \"val\",'test']\n",
"\n",
"class_counter = Counter()\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" \n",
" # 라벨 txt 파일 탐색\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
" \n",
" print(f\"\\n🔍 Checking split: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip(): # 빈 줄 제거\n",
" class_id = line.split()[0] # 첫번째 값 = 클래스\n",
" class_counter[class_id] += 1\n",
"\n",
"# 최종 결과 출력\n",
"print(\"\\n📌 클래스 통계 결과:\")\n",
"for cls, count in sorted(class_counter.items(), key=lambda x: int(x[0])):\n",
" print(f\" - Class {cls}: {count} 개\")\n",
"\n",
"print(f\"\\n총 클래스 종류: {len(class_counter)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "35cf2381",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📂 Updating: train (files: 6471)\n",
"\n",
"📂 Updating: val (files: 548)\n",
"\n",
"📂 Updating: test (files: 1610)\n",
"\n",
"✅ 라벨 클래스 번호 재정렬 완료!\n"
]
}
],
"source": [
"import os\n",
"\n",
"label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels\"\n",
"splits = [\"train\", \"val\", \"test\"]\n",
"\n",
"# 클래스 재매핑 설정\n",
"mapping = {\n",
" \"0\": \"0\", # person stays 0\n",
" \"1\": \"1\", # car stays 1\n",
" \"2\": \"5\", # van -> 5\n",
" \"3\": \"2\", # truck -> 2\n",
" \"4\": \"3\", # bus -> 3\n",
" \"5\": \"4\" # motor -> 4\n",
"}\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
"\n",
" print(f\"\\n📂 Updating: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
"\n",
" new_lines = []\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip():\n",
" parts = line.split()\n",
" cls = parts[0]\n",
" parts[0] = mapping[cls] # 클래스 번호 변경\n",
" new_lines.append(\" \".join(parts) + \"\\n\")\n",
"\n",
" # 덮어쓰기\n",
" with open(file_path, \"w\") as f:\n",
" f.writelines(new_lines)\n",
"\n",
"print(\"\\n✅ 라벨 클래스 번호 재정렬 완료!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3fdd6e44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"🔍 Checking split: train (files: 6471)\n",
"\n",
"🔍 Checking split: val (files: 548)\n",
"\n",
"🔍 Checking split: test (files: 1610)\n",
"\n",
"📌 클래스 통계 결과:\n",
" - Class 0: 147747 개\n",
" - Class 1: 187005 개\n",
" - Class 2: 16284 개\n",
" - Class 3: 9117 개\n",
" - Class 4: 40378 개\n",
" - Class 5: 32702 개\n",
"\n",
"총 클래스 종류: 6\n"
]
}
],
"source": [
"import os\n",
"from collections import Counter\n",
"\n",
"# label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels copy\"\n",
"label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels\"\n",
"splits = [\"train\", \"val\",'test']\n",
"\n",
"class_counter = Counter()\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" \n",
" # 라벨 txt 파일 탐색\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
" \n",
" print(f\"\\n🔍 Checking split: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip(): # 빈 줄 제거\n",
" class_id = line.split()[0] # 첫번째 값 = 클래스\n",
" class_counter[class_id] += 1\n",
"\n",
"# 최종 결과 출력\n",
"print(\"\\n📌 클래스 통계 결과:\")\n",
"for cls, count in sorted(class_counter.items(), key=lambda x: int(x[0])):\n",
" print(f\" - Class {cls}: {count} 개\")\n",
"\n",
"print(f\"\\n총 클래스 종류: {len(class_counter)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ca796e3d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing train: 6471 files\n",
"Processing val: 548 files\n",
"Processing test: 1610 files\n",
"Done. All label files updated!\n"
]
}
],
"source": [
"import os\n",
"from glob import glob\n",
"\n",
"# 라벨 매핑\n",
"mapping = {\n",
" 0: 0,\n",
" 1: 2,\n",
" 2: 5,\n",
" 3: 4,\n",
" 4: 3,\n",
" 5: 1\n",
"}\n",
"\n",
"# labels 폴더 경로\n",
"base_dir = \"/home/cuuva/experiment/datasets/VisDrone/labels\"\n",
"\n",
"# train, val, test 모두 처리\n",
"splits = [\"train\", \"val\", \"test\"]\n",
"\n",
"for split in splits:\n",
" label_dir = os.path.join(base_dir, split)\n",
" txt_files = glob(os.path.join(label_dir, \"*.txt\"))\n",
"\n",
" print(f\"Processing {split}: {len(txt_files)} files\")\n",
"\n",
" for txt_path in txt_files:\n",
" lines = []\n",
" with open(txt_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" parts = line.strip().split()\n",
" if len(parts) < 5:\n",
" continue\n",
"\n",
" old_cls = int(parts[0])\n",
" new_cls = mapping[old_cls]\n",
"\n",
" # 클래스만 변경해서 다시 저장\n",
" parts[0] = str(new_cls)\n",
" lines.append(\" \".join(parts))\n",
"\n",
" with open(txt_path, \"w\") as f:\n",
" f.write(\"\\n\".join(lines))\n",
"\n",
"print(\"Done. All label files updated!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a461857c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"🔍 Checking split: train (files: 6471)\n",
"\n",
"🔍 Checking split: val (files: 548)\n",
"\n",
"🔍 Checking split: test (files: 1610)\n",
"\n",
"📌 클래스 통계 결과:\n",
" - Class 0: 147747 개\n",
" - Class 1: 32702 개\n",
" - Class 2: 187005 개\n",
" - Class 3: 40378 개\n",
" - Class 4: 9117 개\n",
" - Class 5: 16284 개\n",
"\n",
"총 클래스 종류: 6\n"
]
}
],
"source": [
"import os\n",
"from collections import Counter\n",
"\n",
"# label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels copy\"\n",
"label_root = \"/home/cuuva/experiment/datasets/VisDrone/labels\"\n",
"splits = [\"train\", \"val\",'test']\n",
"\n",
"class_counter = Counter()\n",
"\n",
"for split in splits:\n",
" split_path = os.path.join(label_root, split)\n",
" \n",
" # 라벨 txt 파일 탐색\n",
" label_files = [f for f in os.listdir(split_path) if f.endswith(\".txt\")]\n",
" \n",
" print(f\"\\n🔍 Checking split: {split} (files: {len(label_files)})\")\n",
"\n",
" for file in label_files:\n",
" file_path = os.path.join(split_path, file)\n",
" with open(file_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" if line.strip(): # 빈 줄 제거\n",
" class_id = line.split()[0] # 첫번째 값 = 클래스\n",
" class_counter[class_id] += 1\n",
"\n",
"# 최종 결과 출력\n",
"print(\"\\n📌 클래스 통계 결과:\")\n",
"for cls, count in sorted(class_counter.items(), key=lambda x: int(x[0])):\n",
" print(f\" - Class {cls}: {count} 개\")\n",
"\n",
"print(f\"\\n총 클래스 종류: {len(class_counter)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8320460f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "1stagedetect",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

9
vis6class_exp/vis6class.yaml
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# path: /home/cuuva/experiment/datasets/VisDrone # 데이터 경로
train: /home/cuuva/experiment/datasets/VisDrone/images/train
val: /home/cuuva/experiment/datasets/VisDrone/images/val
test: /home/cuuva/experiment/datasets/VisDrone/images/test
# nc: 7
nc: 6
names: ['person','van', 'car', 'motor', 'bus', 'truck']

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vis6class_exp/vis6class_exp.ipynb
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vis6class_exp/vis6class_v8m/6class/args.yaml
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task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/vis6class_exp/vis6class.yaml
epochs: 300
time: null
patience: 40
batch: -1
imgsz: 640
save: true
save_period: -1
cache: false
device: '0'
workers: 8
project: vis6class_v8m
name: 6class
exist_ok: false
pretrained: true
optimizer: AdamW
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
compile: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.001
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: /home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class

101
vis6class_exp/vis6class_v8m/6class/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,74.1002,1.45177,1.3499,0.97119,0.48954,0.30681,0.30529,0.17932,1.46069,1.21004,0.94941,0.0671728,0.000331588,0.000331588
2,148.131,1.42041,1.10112,0.96067,0.47059,0.352,0.36077,0.21327,1.41416,1.03289,0.93623,0.0341706,0.000662727,0.000662727
3,221.858,1.39319,1.03644,0.94917,0.50337,0.35262,0.36769,0.21697,1.42161,1.00914,0.94548,0.00116619,0.000991666,0.000991666
4,295.795,1.38518,1.018,0.94572,0.52072,0.3747,0.39712,0.23517,1.38396,1.00425,0.93809,0.0009901,0.0009901,0.0009901
5,369.646,1.35604,0.97151,0.94079,0.55572,0.40298,0.42724,0.25804,1.36183,0.95484,0.92768,0.0009868,0.0009868,0.0009868
6,443.461,1.32355,0.9374,0.93301,0.56177,0.42047,0.44722,0.27129,1.33378,0.92188,0.92135,0.0009835,0.0009835,0.0009835
7,517.36,1.31262,0.92344,0.92816,0.5582,0.43973,0.4582,0.27517,1.33844,0.92068,0.92221,0.0009802,0.0009802,0.0009802
8,591.194,1.30079,0.90833,0.92641,0.57202,0.41992,0.44361,0.27151,1.31729,0.91243,0.91604,0.0009769,0.0009769,0.0009769
9,665.026,1.29606,0.90336,0.92359,0.57351,0.44143,0.46864,0.2833,1.32878,0.92955,0.91668,0.0009736,0.0009736,0.0009736
10,738.869,1.27877,0.8829,0.92106,0.57844,0.42265,0.45566,0.27703,1.32715,0.89014,0.91463,0.0009703,0.0009703,0.0009703
11,812.532,1.26649,0.86183,0.91754,0.58756,0.44204,0.47371,0.28897,1.29562,0.87513,0.91023,0.000967,0.000967,0.000967
12,886.355,1.26449,0.86083,0.91607,0.60498,0.44684,0.48269,0.29466,1.28648,0.87708,0.90954,0.0009637,0.0009637,0.0009637
13,960.256,1.25465,0.84375,0.91256,0.59391,0.44503,0.48025,0.2962,1.29084,0.87299,0.90876,0.0009604,0.0009604,0.0009604
14,1034.01,1.24925,0.84191,0.91249,0.62119,0.45639,0.49158,0.30217,1.3036,0.86918,0.9129,0.0009571,0.0009571,0.0009571
15,1107.85,1.24384,0.82944,0.90949,0.59543,0.45757,0.49172,0.30291,1.26882,0.84082,0.90175,0.0009538,0.0009538,0.0009538
16,1181.71,1.23645,0.82577,0.90873,0.60781,0.4736,0.50234,0.30809,1.26924,0.83872,0.90204,0.0009505,0.0009505,0.0009505
17,1255.6,1.23074,0.81984,0.90732,0.62542,0.45287,0.50038,0.30833,1.27476,0.83562,0.9022,0.0009472,0.0009472,0.0009472
18,1329.38,1.23136,0.80891,0.90616,0.61224,0.46293,0.50371,0.31596,1.25041,0.82561,0.90028,0.0009439,0.0009439,0.0009439
19,1403.25,1.21353,0.79899,0.90462,0.6217,0.47534,0.51434,0.31848,1.257,0.82485,0.90221,0.0009406,0.0009406,0.0009406
20,1477.11,1.21914,0.79852,0.90496,0.63769,0.45614,0.50191,0.31432,1.25189,0.83266,0.90275,0.0009373,0.0009373,0.0009373
21,1550.91,1.22066,0.79996,0.90467,0.63057,0.47378,0.51174,0.31611,1.25517,0.83242,0.90244,0.000934,0.000934,0.000934
22,1624.82,1.2011,0.78328,0.90034,0.64121,0.46332,0.51413,0.31872,1.24861,0.81903,0.89995,0.0009307,0.0009307,0.0009307
23,1699,1.20225,0.779,0.90028,0.62985,0.47157,0.51082,0.31913,1.24698,0.82095,0.90152,0.0009274,0.0009274,0.0009274
24,1772.77,1.19432,0.76937,0.89887,0.62763,0.47129,0.51185,0.31851,1.24848,0.81293,0.89887,0.0009241,0.0009241,0.0009241
25,1846.59,1.19874,0.77357,0.90067,0.63342,0.48201,0.52219,0.32529,1.24313,0.8181,0.89801,0.0009208,0.0009208,0.0009208
26,1920.33,1.18914,0.7634,0.89848,0.66484,0.46659,0.52273,0.32474,1.23604,0.81586,0.89809,0.0009175,0.0009175,0.0009175
27,1994.05,1.19027,0.75791,0.8968,0.62291,0.486,0.52709,0.32505,1.2401,0.8138,0.89987,0.0009142,0.0009142,0.0009142
28,2067.89,1.18943,0.75845,0.89546,0.62952,0.4772,0.52464,0.33019,1.23751,0.79594,0.89651,0.0009109,0.0009109,0.0009109
29,2141.78,1.18853,0.75727,0.895,0.65237,0.47405,0.52411,0.32989,1.2226,0.79184,0.89237,0.0009076,0.0009076,0.0009076
30,2215.44,1.18059,0.74204,0.89562,0.63721,0.47558,0.51775,0.32138,1.22581,0.80272,0.89263,0.0009043,0.0009043,0.0009043
31,2289.23,1.17573,0.74725,0.89271,0.6468,0.47046,0.52153,0.32638,1.2288,0.79112,0.89563,0.000901,0.000901,0.000901
32,2363.08,1.17888,0.74292,0.89287,0.66591,0.46519,0.52519,0.32722,1.22384,0.79058,0.89314,0.0008977,0.0008977,0.0008977
33,2436.97,1.17005,0.73613,0.89263,0.64243,0.47902,0.52435,0.32877,1.21923,0.78505,0.89301,0.0008944,0.0008944,0.0008944
34,2512.07,1.1632,0.72889,0.89069,0.64894,0.4685,0.51891,0.322,1.23532,0.80206,0.89608,0.0008911,0.0008911,0.0008911
35,2587.17,1.16201,0.72743,0.88952,0.64691,0.46724,0.52403,0.32852,1.22295,0.79486,0.89299,0.0008878,0.0008878,0.0008878
36,2660.64,1.16055,0.72498,0.8894,0.63265,0.48319,0.52265,0.32769,1.22537,0.79251,0.89651,0.0008845,0.0008845,0.0008845
37,2735.17,1.15708,0.72049,0.8888,0.63683,0.4837,0.52562,0.33086,1.2182,0.77906,0.89215,0.0008812,0.0008812,0.0008812
38,2809.44,1.16261,0.72399,0.88985,0.66075,0.47968,0.53176,0.33366,1.21256,0.77985,0.8892,0.0008779,0.0008779,0.0008779
39,2883.14,1.158,0.71599,0.88784,0.63471,0.48871,0.52989,0.3319,1.21403,0.78409,0.8918,0.0008746,0.0008746,0.0008746
40,2955.65,1.14864,0.70722,0.888,0.66291,0.48038,0.53138,0.33193,1.2104,0.77767,0.89061,0.0008713,0.0008713,0.0008713
41,3028.11,1.14859,0.70842,0.88577,0.65266,0.47701,0.53123,0.33279,1.20868,0.7815,0.89215,0.000868,0.000868,0.000868
42,3100.49,1.14499,0.70132,0.88634,0.65735,0.48205,0.52865,0.33116,1.21802,0.78321,0.89139,0.0008647,0.0008647,0.0008647
43,3172.86,1.14437,0.70064,0.88572,0.65954,0.48343,0.53384,0.33578,1.20273,0.77706,0.88826,0.0008614,0.0008614,0.0008614
44,3245.23,1.13991,0.69422,0.88404,0.65166,0.49156,0.53855,0.33644,1.21709,0.77893,0.89371,0.0008581,0.0008581,0.0008581
45,3317.74,1.14372,0.69838,0.88459,0.64606,0.48434,0.53244,0.3329,1.2121,0.7753,0.89105,0.0008548,0.0008548,0.0008548
46,3390.06,1.1414,0.69568,0.88466,0.63221,0.49846,0.53759,0.33808,1.20551,0.76904,0.88981,0.0008515,0.0008515,0.0008515
47,3462.65,1.13173,0.68769,0.88171,0.65224,0.49405,0.54014,0.34013,1.19997,0.77476,0.88795,0.0008482,0.0008482,0.0008482
48,3535.03,1.13729,0.68734,0.88423,0.66475,0.49016,0.53842,0.33647,1.20183,0.77463,0.88949,0.0008449,0.0008449,0.0008449
49,3607.4,1.12732,0.68295,0.88225,0.64287,0.50616,0.54496,0.33972,1.20181,0.76998,0.88762,0.0008416,0.0008416,0.0008416
50,3679.65,1.12769,0.67455,0.88072,0.64257,0.49628,0.53713,0.33665,1.19593,0.77052,0.88778,0.0008383,0.0008383,0.0008383
51,3752.44,1.12459,0.67369,0.87994,0.66438,0.49439,0.54115,0.341,1.20121,0.76558,0.89086,0.000835,0.000835,0.000835
52,3824.77,1.12492,0.67441,0.88148,0.64922,0.49792,0.54411,0.34039,1.20228,0.77638,0.8886,0.0008317,0.0008317,0.0008317
53,3897.2,1.12367,0.66967,0.87916,0.67566,0.48504,0.5417,0.34078,1.19791,0.76749,0.88836,0.0008284,0.0008284,0.0008284
54,3969.58,1.11978,0.6677,0.87803,0.65944,0.48818,0.53831,0.3412,1.20764,0.76303,0.89063,0.0008251,0.0008251,0.0008251
55,4042,1.11091,0.65901,0.8786,0.66716,0.48986,0.54491,0.34139,1.1979,0.76396,0.8889,0.0008218,0.0008218,0.0008218
56,4114.38,1.11294,0.65819,0.87891,0.65954,0.49612,0.54292,0.3413,1.19875,0.77262,0.88994,0.0008185,0.0008185,0.0008185
57,4187.54,1.10293,0.65115,0.87574,0.66227,0.49126,0.54238,0.34046,1.19886,0.77201,0.88783,0.0008152,0.0008152,0.0008152
58,4259.88,1.10904,0.65535,0.87706,0.66581,0.49197,0.54369,0.34109,1.19763,0.76306,0.89061,0.0008119,0.0008119,0.0008119
59,4332.12,1.11256,0.65495,0.8777,0.67663,0.47929,0.53748,0.33687,1.19974,0.77325,0.88717,0.0008086,0.0008086,0.0008086
60,4404.5,1.1163,0.65534,0.87849,0.6446,0.51004,0.54834,0.34649,1.19826,0.77172,0.8894,0.0008053,0.0008053,0.0008053
61,4476.92,1.106,0.64779,0.87606,0.65811,0.4988,0.54471,0.34462,1.19408,0.76817,0.88797,0.000802,0.000802,0.000802
62,4549.25,1.11071,0.65031,0.87666,0.64364,0.49678,0.53968,0.33836,1.20517,0.772,0.88932,0.0007987,0.0007987,0.0007987
63,4621.53,1.10201,0.64659,0.87538,0.65773,0.49565,0.54188,0.34107,1.19662,0.76737,0.88941,0.0007954,0.0007954,0.0007954
64,4694.02,1.1019,0.64234,0.87478,0.66535,0.49353,0.54393,0.34271,1.19865,0.78081,0.88816,0.0007921,0.0007921,0.0007921
65,4766.38,1.0982,0.64241,0.87394,0.67216,0.49012,0.53965,0.33917,1.19457,0.77693,0.88879,0.0007888,0.0007888,0.0007888
66,4838.58,1.09651,0.63617,0.8733,0.66479,0.49593,0.5458,0.34371,1.19311,0.76764,0.88848,0.0007855,0.0007855,0.0007855
67,4911.39,1.09537,0.63411,0.87258,0.65958,0.49278,0.53976,0.33956,1.19891,0.77303,0.8899,0.0007822,0.0007822,0.0007822
68,4983.7,1.09516,0.63298,0.87366,0.63835,0.50848,0.54478,0.34088,1.19916,0.77314,0.88865,0.0007789,0.0007789,0.0007789
69,5055.91,1.0883,0.62911,0.87306,0.66238,0.4999,0.54766,0.34407,1.19216,0.76919,0.88575,0.0007756,0.0007756,0.0007756
70,5128.17,1.08008,0.62228,0.87142,0.65797,0.50159,0.54531,0.34408,1.19755,0.76898,0.88762,0.0007723,0.0007723,0.0007723
71,5200.48,1.09146,0.62682,0.87329,0.66136,0.5004,0.54511,0.34296,1.19562,0.76526,0.88845,0.000769,0.000769,0.000769
72,5272.75,1.08888,0.62716,0.87175,0.66111,0.49765,0.5436,0.34159,1.19342,0.76764,0.88763,0.0007657,0.0007657,0.0007657
73,5345.13,1.07556,0.61709,0.86945,0.65721,0.49722,0.54432,0.34278,1.19749,0.76871,0.88775,0.0007624,0.0007624,0.0007624
74,5417.38,1.08477,0.6175,0.86957,0.65787,0.50006,0.54727,0.34465,1.19263,0.76693,0.8888,0.0007591,0.0007591,0.0007591
75,5489.65,1.07628,0.61429,0.86938,0.65673,0.50398,0.5438,0.34317,1.20072,0.77864,0.8908,0.0007558,0.0007558,0.0007558
76,5561.86,1.08551,0.62133,0.87045,0.64583,0.51014,0.5445,0.34393,1.19578,0.7748,0.88965,0.0007525,0.0007525,0.0007525
77,5634.22,1.07656,0.61527,0.87059,0.66126,0.50183,0.54387,0.34368,1.19785,0.77115,0.88783,0.0007492,0.0007492,0.0007492
78,5706.51,1.07227,0.60475,0.8687,0.65533,0.50521,0.5439,0.34286,1.19838,0.77319,0.89088,0.0007459,0.0007459,0.0007459
79,5778.88,1.06573,0.60297,0.86757,0.66496,0.50694,0.54932,0.34623,1.19881,0.7633,0.89168,0.0007426,0.0007426,0.0007426
80,5851.34,1.07305,0.61008,0.86604,0.64342,0.50819,0.546,0.3439,1.1967,0.76852,0.88934,0.0007393,0.0007393,0.0007393
81,5923.65,1.06842,0.60472,0.86817,0.65509,0.50407,0.54473,0.3429,1.18831,0.77033,0.88778,0.000736,0.000736,0.000736
82,5995.96,1.05868,0.59483,0.86755,0.64527,0.50256,0.54172,0.34184,1.19134,0.77004,0.88796,0.0007327,0.0007327,0.0007327
83,6068.46,1.06946,0.60458,0.86802,0.65247,0.50126,0.54538,0.34521,1.19352,0.76936,0.88764,0.0007294,0.0007294,0.0007294
84,6140.85,1.0633,0.59609,0.86778,0.66003,0.49853,0.54345,0.34426,1.19173,0.76804,0.88891,0.0007261,0.0007261,0.0007261
85,6213.21,1.06255,0.59798,0.86686,0.64389,0.50609,0.53928,0.34,1.19596,0.78076,0.88901,0.0007228,0.0007228,0.0007228
86,6285.59,1.05872,0.59551,0.86492,0.66053,0.49411,0.54097,0.3425,1.18957,0.77201,0.88797,0.0007195,0.0007195,0.0007195
87,6357.89,1.06588,0.60055,0.86629,0.65764,0.50591,0.54331,0.34451,1.18845,0.76804,0.8894,0.0007162,0.0007162,0.0007162
88,6430.13,1.05583,0.59233,0.86545,0.66929,0.49807,0.5432,0.34192,1.19021,0.76795,0.88885,0.0007129,0.0007129,0.0007129
89,6502.44,1.05788,0.59212,0.86488,0.65811,0.50078,0.54185,0.33991,1.18722,0.76635,0.88794,0.0007096,0.0007096,0.0007096
90,6574.82,1.06001,0.59024,0.86442,0.65954,0.4991,0.5409,0.34017,1.19106,0.76648,0.88913,0.0007063,0.0007063,0.0007063
91,6647.18,1.05431,0.58916,0.86293,0.65832,0.49574,0.54238,0.34164,1.19167,0.77427,0.88956,0.000703,0.000703,0.000703
92,6719.49,1.05512,0.58783,0.86384,0.66736,0.49951,0.54549,0.34382,1.19155,0.77794,0.88943,0.0006997,0.0006997,0.0006997
93,6791.88,1.0574,0.58857,0.86407,0.658,0.50624,0.54582,0.34334,1.1921,0.77577,0.89028,0.0006964,0.0006964,0.0006964
94,6864.22,1.04456,0.58057,0.86156,0.67277,0.49883,0.54728,0.34536,1.19149,0.77641,0.88997,0.0006931,0.0006931,0.0006931
95,6936.57,1.03756,0.57759,0.86071,0.65774,0.5049,0.54407,0.34391,1.19252,0.77677,0.89113,0.0006898,0.0006898,0.0006898
96,7008.71,1.0463,0.58003,0.86268,0.64992,0.50999,0.54584,0.34515,1.19362,0.77831,0.89113,0.0006865,0.0006865,0.0006865
97,7081.06,1.0451,0.57911,0.86158,0.66347,0.50119,0.54566,0.34544,1.18953,0.77758,0.88891,0.0006832,0.0006832,0.0006832
98,7153.43,1.04041,0.57505,0.86344,0.66747,0.49646,0.54544,0.34505,1.18929,0.77578,0.88913,0.0006799,0.0006799,0.0006799
99,7225.84,1.0414,0.57453,0.86095,0.66874,0.49732,0.54594,0.3456,1.18851,0.77512,0.8899,0.0006766,0.0006766,0.0006766
100,7298.73,1.03886,0.57283,0.86126,0.66191,0.49803,0.54386,0.34598,1.1903,0.78002,0.89023,0.0006733,0.0006733,0.0006733
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 74.1002 1.45177 1.3499 0.97119 0.48954 0.30681 0.30529 0.17932 1.46069 1.21004 0.94941 0.0671728 0.000331588 0.000331588
3 2 148.131 1.42041 1.10112 0.96067 0.47059 0.352 0.36077 0.21327 1.41416 1.03289 0.93623 0.0341706 0.000662727 0.000662727
4 3 221.858 1.39319 1.03644 0.94917 0.50337 0.35262 0.36769 0.21697 1.42161 1.00914 0.94548 0.00116619 0.000991666 0.000991666
5 4 295.795 1.38518 1.018 0.94572 0.52072 0.3747 0.39712 0.23517 1.38396 1.00425 0.93809 0.0009901 0.0009901 0.0009901
6 5 369.646 1.35604 0.97151 0.94079 0.55572 0.40298 0.42724 0.25804 1.36183 0.95484 0.92768 0.0009868 0.0009868 0.0009868
7 6 443.461 1.32355 0.9374 0.93301 0.56177 0.42047 0.44722 0.27129 1.33378 0.92188 0.92135 0.0009835 0.0009835 0.0009835
8 7 517.36 1.31262 0.92344 0.92816 0.5582 0.43973 0.4582 0.27517 1.33844 0.92068 0.92221 0.0009802 0.0009802 0.0009802
9 8 591.194 1.30079 0.90833 0.92641 0.57202 0.41992 0.44361 0.27151 1.31729 0.91243 0.91604 0.0009769 0.0009769 0.0009769
10 9 665.026 1.29606 0.90336 0.92359 0.57351 0.44143 0.46864 0.2833 1.32878 0.92955 0.91668 0.0009736 0.0009736 0.0009736
11 10 738.869 1.27877 0.8829 0.92106 0.57844 0.42265 0.45566 0.27703 1.32715 0.89014 0.91463 0.0009703 0.0009703 0.0009703
12 11 812.532 1.26649 0.86183 0.91754 0.58756 0.44204 0.47371 0.28897 1.29562 0.87513 0.91023 0.000967 0.000967 0.000967
13 12 886.355 1.26449 0.86083 0.91607 0.60498 0.44684 0.48269 0.29466 1.28648 0.87708 0.90954 0.0009637 0.0009637 0.0009637
14 13 960.256 1.25465 0.84375 0.91256 0.59391 0.44503 0.48025 0.2962 1.29084 0.87299 0.90876 0.0009604 0.0009604 0.0009604
15 14 1034.01 1.24925 0.84191 0.91249 0.62119 0.45639 0.49158 0.30217 1.3036 0.86918 0.9129 0.0009571 0.0009571 0.0009571
16 15 1107.85 1.24384 0.82944 0.90949 0.59543 0.45757 0.49172 0.30291 1.26882 0.84082 0.90175 0.0009538 0.0009538 0.0009538
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vis6class_exp/vis6class_v8m/6class2/args.yaml
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task: detect
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model: yolov8m.pt
data: /home/cuuva/experiment/vis6class_exp/vis6class.yaml
epochs: 600
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187
vis6class_exp/vis6class_v8m/6class2/results.csv
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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
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23,1665.36,1.20261,0.7808,0.90082,0.63045,0.4775,0.51558,0.32076,1.25094,0.81355,0.89995,0.0009637,0.0009637,0.0009637
24,1737.64,1.19467,0.77229,0.89903,0.62333,0.46517,0.50437,0.31446,1.23966,0.81092,0.90075,0.00096205,0.00096205,0.00096205
25,1810.04,1.19834,0.77569,0.90157,0.63327,0.47282,0.5167,0.32484,1.24061,0.82096,0.89414,0.0009604,0.0009604,0.0009604
26,1882.36,1.19423,0.76706,0.90021,0.63337,0.46873,0.52114,0.32483,1.23635,0.81724,0.89706,0.00095875,0.00095875,0.00095875
27,1954.69,1.19284,0.76055,0.89767,0.63242,0.48032,0.52245,0.3258,1.25015,0.8098,0.90155,0.0009571,0.0009571,0.0009571
28,2027.05,1.19259,0.76047,0.896,0.62508,0.47202,0.51627,0.32041,1.25853,0.81012,0.90283,0.00095545,0.00095545,0.00095545
29,2099.39,1.19025,0.75798,0.89554,0.64925,0.48362,0.53221,0.33325,1.22656,0.8042,0.89485,0.0009538,0.0009538,0.0009538
30,2171.61,1.18241,0.74459,0.89648,0.64479,0.48298,0.52818,0.3286,1.22912,0.79349,0.89775,0.00095215,0.00095215,0.00095215
31,2243.86,1.17875,0.75268,0.89338,0.64261,0.4879,0.53199,0.33534,1.22343,0.79848,0.89628,0.0009505,0.0009505,0.0009505
32,2316.19,1.18473,0.75119,0.89432,0.62964,0.48815,0.52537,0.32652,1.22146,0.80032,0.89288,0.00094885,0.00094885,0.00094885
33,2388.56,1.17415,0.74142,0.89419,0.65025,0.47264,0.52712,0.32948,1.22089,0.78761,0.89377,0.0009472,0.0009472,0.0009472
34,2460.92,1.16609,0.73147,0.89178,0.6414,0.47677,0.52358,0.32527,1.22078,0.79837,0.8932,0.00094555,0.00094555,0.00094555
35,2533.91,1.16549,0.73254,0.89103,0.64599,0.48047,0.52853,0.32827,1.2239,0.78803,0.89588,0.0009439,0.0009439,0.0009439
36,2608.02,1.1644,0.73027,0.89064,0.64509,0.48353,0.53129,0.33218,1.21224,0.78572,0.89136,0.00094225,0.00094225,0.00094225
37,2681.66,1.16125,0.7269,0.88967,0.65259,0.4918,0.53473,0.33366,1.21601,0.78924,0.89365,0.0009406,0.0009406,0.0009406
38,2754,1.16903,0.73047,0.89189,0.66162,0.48744,0.53717,0.33566,1.21969,0.78049,0.89523,0.00093895,0.00093895,0.00093895
39,2826.34,1.16142,0.71879,0.88806,0.6421,0.48384,0.52918,0.33237,1.21605,0.79474,0.89238,0.0009373,0.0009373,0.0009373
40,2898.6,1.15578,0.71459,0.88879,0.65262,0.49704,0.54023,0.34085,1.21222,0.7766,0.894,0.00093565,0.00093565,0.00093565
41,2971.03,1.15058,0.71198,0.88627,0.64881,0.49684,0.5465,0.34272,1.21169,0.77826,0.89543,0.000934,0.000934,0.000934
42,3043.4,1.15016,0.70886,0.88804,0.66036,0.49255,0.54224,0.3392,1.21453,0.77956,0.89251,0.00093235,0.00093235,0.00093235
43,3115.8,1.14813,0.70686,0.88723,0.64109,0.50083,0.54222,0.34065,1.20817,0.77646,0.88969,0.0009307,0.0009307,0.0009307
44,3188.07,1.1452,0.70293,0.88504,0.65725,0.49637,0.53884,0.33921,1.21544,0.77561,0.89433,0.00092905,0.00092905,0.00092905
45,3260.45,1.14926,0.70372,0.88566,0.65689,0.4884,0.53707,0.3368,1.21143,0.77677,0.89118,0.0009274,0.0009274,0.0009274
46,3332.69,1.14623,0.7,0.88575,0.66543,0.48589,0.53812,0.33877,1.21676,0.77225,0.8931,0.00092575,0.00092575,0.00092575
47,3405.23,1.13626,0.69402,0.88284,0.65806,0.49123,0.54479,0.34009,1.20989,0.78151,0.89305,0.0009241,0.0009241,0.0009241
48,3477.59,1.14177,0.69621,0.88564,0.6595,0.49283,0.54478,0.34213,1.20568,0.78292,0.89117,0.00092245,0.00092245,0.00092245
49,3549.97,1.13236,0.68789,0.88352,0.66013,0.48967,0.53958,0.33687,1.21342,0.7733,0.89422,0.0009208,0.0009208,0.0009208
50,3622.23,1.13373,0.6825,0.88151,0.66861,0.49138,0.5436,0.34066,1.21085,0.7781,0.89259,0.00091915,0.00091915,0.00091915
51,3694.9,1.12987,0.68214,0.88055,0.64976,0.50479,0.54459,0.34273,1.20041,0.76117,0.88906,0.0009175,0.0009175,0.0009175
52,3769.1,1.12952,0.68015,0.88236,0.67014,0.49388,0.54785,0.34431,1.20305,0.76721,0.89067,0.00091585,0.00091585,0.00091585
53,3845.24,1.12818,0.67503,0.88015,0.65329,0.50391,0.54998,0.34518,1.20071,0.77044,0.88734,0.0009142,0.0009142,0.0009142
54,3920.19,1.12539,0.67421,0.87931,0.67196,0.48386,0.54301,0.34231,1.19752,0.77885,0.88972,0.00091255,0.00091255,0.00091255
55,3993.34,1.11628,0.66518,0.87976,0.67265,0.50073,0.55034,0.34705,1.19835,0.7641,0.88766,0.0009109,0.0009109,0.0009109
56,4065.6,1.11883,0.66422,0.88026,0.66827,0.486,0.54042,0.34098,1.19894,0.77048,0.89036,0.00090925,0.00090925,0.00090925
57,4138.08,1.10893,0.65857,0.87715,0.65354,0.49715,0.54175,0.34216,1.20237,0.77624,0.88906,0.0009076,0.0009076,0.0009076
58,4210.32,1.11571,0.66504,0.87863,0.65137,0.49786,0.54437,0.3412,1.19606,0.76897,0.88918,0.00090595,0.00090595,0.00090595
59,4282.52,1.11779,0.66166,0.87904,0.64097,0.50391,0.5457,0.34429,1.19209,0.76886,0.88617,0.0009043,0.0009043,0.0009043
60,4354.8,1.12066,0.6612,0.87997,0.66987,0.4985,0.54409,0.34318,1.20218,0.77232,0.88938,0.00090265,0.00090265,0.00090265
61,4427.07,1.11248,0.65849,0.87838,0.67474,0.49368,0.54656,0.34726,1.19442,0.77499,0.88719,0.000901,0.000901,0.000901
62,4499.42,1.11531,0.65582,0.87789,0.68688,0.491,0.54705,0.34517,1.2005,0.76907,0.88667,0.00089935,0.00089935,0.00089935
63,4571.81,1.10904,0.65513,0.87686,0.67222,0.4951,0.54598,0.34407,1.19771,0.77059,0.88897,0.0008977,0.0008977,0.0008977
64,4644.23,1.10843,0.65289,0.87677,0.67669,0.48893,0.54573,0.34342,1.19382,0.7693,0.88624,0.00089605,0.00089605,0.00089605
65,4716.54,1.10467,0.65013,0.87612,0.66774,0.4941,0.54817,0.34721,1.1961,0.76173,0.88791,0.0008944,0.0008944,0.0008944
66,4788.72,1.10409,0.64634,0.87544,0.64695,0.50827,0.54607,0.34572,1.19439,0.76959,0.88871,0.00089275,0.00089275,0.00089275
67,4861.12,1.10075,0.63885,0.87408,0.6651,0.50159,0.54995,0.34507,1.19862,0.77373,0.88935,0.0008911,0.0008911,0.0008911
68,4933.53,1.10218,0.63962,0.87535,0.66654,0.50152,0.54928,0.34517,1.19865,0.77508,0.88987,0.00088945,0.00088945,0.00088945
69,5005.94,1.09538,0.6366,0.87412,0.67059,0.50131,0.55087,0.34788,1.19825,0.76778,0.89022,0.0008878,0.0008878,0.0008878
70,5078.4,1.08488,0.6294,0.87246,0.64927,0.50679,0.55076,0.34633,1.19907,0.77485,0.88867,0.00088615,0.00088615,0.00088615
71,5150.79,1.09718,0.6346,0.87436,0.65603,0.50543,0.54586,0.34482,1.20113,0.77295,0.88899,0.0008845,0.0008845,0.0008845
72,5223.31,1.09698,0.63752,0.87336,0.67832,0.49065,0.54774,0.34549,1.19276,0.76489,0.88706,0.00088285,0.00088285,0.00088285
73,5295.86,1.08718,0.62772,0.87311,0.66895,0.50115,0.55087,0.34903,1.19536,0.76795,0.88953,0.0008812,0.0008812,0.0008812
74,5368.31,1.09173,0.62591,0.87142,0.65321,0.50049,0.54909,0.346,1.19257,0.77368,0.88797,0.00087955,0.00087955,0.00087955
75,5440.71,1.08386,0.62446,0.8713,0.66003,0.50506,0.55198,0.34786,1.18925,0.7653,0.8862,0.0008779,0.0008779,0.0008779
76,5513.13,1.09068,0.62616,0.87191,0.67411,0.49852,0.54843,0.34741,1.18911,0.77415,0.8881,0.00087625,0.00087625,0.00087625
77,5585.59,1.08116,0.6205,0.87149,0.66281,0.50779,0.5503,0.34646,1.19151,0.7642,0.8886,0.0008746,0.0008746,0.0008746
78,5658.01,1.0815,0.61432,0.87114,0.67578,0.49892,0.54563,0.34599,1.19142,0.76895,0.88728,0.00087295,0.00087295,0.00087295
79,5730.6,1.07114,0.60869,0.86868,0.65774,0.50059,0.54476,0.34497,1.19638,0.76712,0.8891,0.0008713,0.0008713,0.0008713
80,5803.04,1.08027,0.61861,0.86776,0.66866,0.50061,0.54707,0.34764,1.18702,0.77129,0.88689,0.00086965,0.00086965,0.00086965
81,5875.4,1.07542,0.61253,0.86973,0.65263,0.51314,0.54969,0.34847,1.18886,0.77443,0.8863,0.000868,0.000868,0.000868
82,5947.92,1.06649,0.60695,0.86941,0.67022,0.49905,0.54703,0.34807,1.18915,0.77419,0.8855,0.00086635,0.00086635,0.00086635
83,6020.55,1.07665,0.61223,0.86915,0.65365,0.51121,0.55139,0.34838,1.19204,0.76962,0.88737,0.0008647,0.0008647,0.0008647
84,6093.04,1.07314,0.60612,0.86995,0.65331,0.5057,0.5475,0.34768,1.18985,0.77056,0.8878,0.00086305,0.00086305,0.00086305
85,6165.48,1.06819,0.60353,0.86834,0.6713,0.50264,0.54946,0.34815,1.19171,0.77758,0.88834,0.0008614,0.0008614,0.0008614
86,6237.95,1.06621,0.60231,0.86677,0.65965,0.5109,0.55417,0.35043,1.18916,0.77287,0.88672,0.00085975,0.00085975,0.00085975
87,6310.36,1.07447,0.60976,0.86829,0.66464,0.50481,0.55104,0.34944,1.19461,0.77689,0.88772,0.0008581,0.0008581,0.0008581
88,6382.69,1.06577,0.60213,0.86709,0.6699,0.49989,0.54979,0.3485,1.19404,0.78195,0.88725,0.00085645,0.00085645,0.00085645
89,6455.07,1.06648,0.60041,0.86731,0.66336,0.50623,0.54905,0.34826,1.19608,0.78134,0.88844,0.0008548,0.0008548,0.0008548
90,6527.56,1.06808,0.59934,0.86625,0.68432,0.49654,0.55111,0.34984,1.18522,0.77544,0.88508,0.00085315,0.00085315,0.00085315
91,6600.01,1.06304,0.5972,0.86442,0.66921,0.50346,0.55062,0.34804,1.19016,0.78142,0.88589,0.0008515,0.0008515,0.0008515
92,6672.42,1.06414,0.59905,0.8658,0.66221,0.50413,0.55059,0.34846,1.18953,0.78374,0.88744,0.00084985,0.00084985,0.00084985
93,6744.94,1.06828,0.59974,0.86645,0.66419,0.50561,0.54935,0.34757,1.18712,0.78315,0.88785,0.0008482,0.0008482,0.0008482
94,6817.45,1.05625,0.59019,0.86313,0.67126,0.49933,0.54701,0.34703,1.19233,0.78203,0.88718,0.00084655,0.00084655,0.00084655
95,6889.91,1.04605,0.58626,0.86262,0.66929,0.50333,0.54935,0.3485,1.1965,0.78294,0.88792,0.0008449,0.0008449,0.0008449
96,6962.23,1.05671,0.58926,0.86558,0.66283,0.50822,0.54891,0.34689,1.19646,0.78104,0.88824,0.00084325,0.00084325,0.00084325
97,7034.75,1.05632,0.5896,0.86455,0.66684,0.50376,0.54826,0.34849,1.18844,0.77599,0.88548,0.0008416,0.0008416,0.0008416
98,7107.18,1.05158,0.58572,0.86638,0.66431,0.50762,0.55083,0.34833,1.18448,0.78099,0.88612,0.00083995,0.00083995,0.00083995
99,7179.65,1.05229,0.58653,0.86332,0.6671,0.50746,0.55297,0.34899,1.18526,0.77791,0.88599,0.0008383,0.0008383,0.0008383
100,7252.18,1.04745,0.58254,0.86341,0.6789,0.50191,0.55198,0.34949,1.1881,0.78241,0.88599,0.00083665,0.00083665,0.00083665
101,7324.67,1.04421,0.58074,0.86384,0.66999,0.50648,0.55072,0.34794,1.18697,0.78611,0.88511,0.000835,0.000835,0.000835
102,7397,1.04692,0.58175,0.86325,0.67008,0.50553,0.55281,0.35031,1.18542,0.7825,0.885,0.00083335,0.00083335,0.00083335
103,7469.41,1.03982,0.57844,0.86101,0.66417,0.50691,0.55275,0.34957,1.18627,0.7823,0.88571,0.0008317,0.0008317,0.0008317
104,7541.86,1.04371,0.57491,0.86185,0.67503,0.50189,0.55202,0.34997,1.18967,0.78245,0.88551,0.00083005,0.00083005,0.00083005
105,7614.23,1.04597,0.57929,0.86255,0.6553,0.50965,0.5512,0.34857,1.18904,0.78275,0.88577,0.0008284,0.0008284,0.0008284
106,7686.71,1.04398,0.5786,0.86113,0.666,0.50359,0.55039,0.34883,1.18945,0.78219,0.88613,0.00082675,0.00082675,0.00082675
107,7759.27,1.04157,0.57522,0.86366,0.67416,0.50087,0.55146,0.34924,1.18721,0.77777,0.88542,0.0008251,0.0008251,0.0008251
108,7831.72,1.03951,0.57104,0.86047,0.67089,0.50358,0.55214,0.34892,1.1871,0.77592,0.88528,0.00082345,0.00082345,0.00082345
109,7904.2,1.03631,0.57295,0.86018,0.66284,0.50727,0.5502,0.34795,1.18772,0.78101,0.88523,0.0008218,0.0008218,0.0008218
110,7976.72,1.0321,0.5684,0.85976,0.66292,0.51125,0.5504,0.34829,1.18671,0.78361,0.88566,0.00082015,0.00082015,0.00082015
111,8049.11,1.04522,0.57368,0.86045,0.6647,0.50954,0.54928,0.34725,1.18904,0.78506,0.88617,0.0008185,0.0008185,0.0008185
112,8121.54,1.02903,0.56446,0.85918,0.66793,0.50688,0.54791,0.34675,1.19012,0.786,0.88579,0.00081685,0.00081685,0.00081685
113,8194.08,1.02992,0.56434,0.85783,0.66144,0.50789,0.54764,0.34639,1.18969,0.78753,0.88629,0.0008152,0.0008152,0.0008152
114,8266.62,1.03075,0.56575,0.85941,0.65667,0.50994,0.54795,0.34654,1.19041,0.78414,0.88658,0.00081355,0.00081355,0.00081355
115,8339.19,1.02835,0.5644,0.8574,0.66666,0.50552,0.54735,0.3464,1.19119,0.78495,0.88599,0.0008119,0.0008119,0.0008119
116,8411.56,1.01978,0.55888,0.85873,0.66621,0.50761,0.54764,0.34703,1.19046,0.7865,0.88624,0.00081025,0.00081025,0.00081025
117,8484.1,1.02243,0.55955,0.85711,0.66755,0.50739,0.5477,0.34661,1.18868,0.78558,0.88634,0.0008086,0.0008086,0.0008086
118,8556.47,1.03104,0.56307,0.85839,0.66031,0.50842,0.54797,0.34704,1.188,0.78695,0.88656,0.00080695,0.00080695,0.00080695
119,8628.94,1.02169,0.55988,0.85775,0.6638,0.50555,0.54891,0.34744,1.18641,0.78644,0.88657,0.0008053,0.0008053,0.0008053
120,8701.44,1.02222,0.55843,0.85909,0.66898,0.50549,0.54784,0.34679,1.18874,0.7846,0.88729,0.00080365,0.00080365,0.00080365
121,8774.03,1.02254,0.55752,0.85708,0.67184,0.50251,0.54705,0.34677,1.18981,0.7854,0.88731,0.000802,0.000802,0.000802
122,8846.42,1.0239,0.55665,0.85775,0.67363,0.5018,0.54646,0.34543,1.19008,0.78554,0.88703,0.00080035,0.00080035,0.00080035
123,8918.78,1.02307,0.55869,0.85649,0.67682,0.50052,0.54602,0.34592,1.19072,0.7843,0.88743,0.0007987,0.0007987,0.0007987
124,8991.22,1.01487,0.55568,0.8563,0.67618,0.50111,0.54642,0.34543,1.19118,0.78519,0.88792,0.00079705,0.00079705,0.00079705
125,9063.57,1.01955,0.55719,0.85778,0.67354,0.50214,0.54602,0.34509,1.19089,0.78494,0.88826,0.0007954,0.0007954,0.0007954
126,9136.06,1.02061,0.55542,0.85773,0.6729,0.50007,0.54638,0.34589,1.19074,0.78673,0.88782,0.00079375,0.00079375,0.00079375
127,9208.59,1.01356,0.54978,0.85547,0.67228,0.50148,0.54631,0.34622,1.1893,0.78797,0.88756,0.0007921,0.0007921,0.0007921
128,9280.96,1.01064,0.54896,0.8559,0.66366,0.50874,0.54766,0.34665,1.18891,0.78686,0.88707,0.00079045,0.00079045,0.00079045
129,9353.4,1.01602,0.55393,0.85588,0.65911,0.51045,0.54755,0.34622,1.18735,0.78552,0.8869,0.0007888,0.0007888,0.0007888
130,9425.82,1.00902,0.54897,0.85418,0.666,0.50742,0.54779,0.34639,1.18783,0.78486,0.88703,0.00078715,0.00078715,0.00078715
131,9498.38,1.01303,0.54922,0.85326,0.67766,0.49972,0.54754,0.34626,1.18791,0.78449,0.88679,0.0007855,0.0007855,0.0007855
132,9570.79,1.00704,0.54679,0.85556,0.67253,0.50363,0.54785,0.34681,1.18843,0.78508,0.88681,0.00078385,0.00078385,0.00078385
133,9643.3,1.0056,0.54446,0.85402,0.67008,0.50449,0.54796,0.34688,1.18859,0.78452,0.88676,0.0007822,0.0007822,0.0007822
134,9715.76,1.00696,0.54671,0.85444,0.67414,0.50404,0.54867,0.34642,1.18809,0.78589,0.88692,0.00078055,0.00078055,0.00078055
135,9788.2,1.0064,0.54299,0.85367,0.67565,0.50396,0.54783,0.34638,1.18842,0.78707,0.88758,0.0007789,0.0007789,0.0007789
136,9860.68,1.00381,0.54353,0.85369,0.6727,0.50639,0.5463,0.34641,1.18911,0.78648,0.88774,0.00077725,0.00077725,0.00077725
137,9933.08,1.00401,0.54291,0.85244,0.666,0.51047,0.54611,0.34621,1.18956,0.78679,0.88803,0.0007756,0.0007756,0.0007756
138,10005.6,1.00042,0.54213,0.85324,0.66882,0.50844,0.54698,0.34702,1.18966,0.78559,0.88815,0.00077395,0.00077395,0.00077395
139,10078.1,1.00312,0.54261,0.85251,0.66976,0.50651,0.54677,0.34734,1.19042,0.78425,0.8887,0.0007723,0.0007723,0.0007723
140,10150.5,1.00054,0.53983,0.85356,0.67176,0.50674,0.54808,0.34774,1.19207,0.78421,0.88932,0.00077065,0.00077065,0.00077065
141,10222.9,0.99637,0.53862,0.85336,0.67533,0.50703,0.54871,0.34831,1.19182,0.78425,0.88911,0.000769,0.000769,0.000769
142,10295.4,0.99712,0.53591,0.85239,0.67619,0.50661,0.54881,0.34865,1.1917,0.78417,0.88862,0.00076735,0.00076735,0.00076735
143,10368,0.99763,0.53804,0.85085,0.67451,0.50728,0.54913,0.34842,1.1913,0.7844,0.88852,0.0007657,0.0007657,0.0007657
144,10440.5,0.99905,0.54047,0.85247,0.66851,0.51014,0.54896,0.34842,1.1907,0.78519,0.88855,0.00076405,0.00076405,0.00076405
145,10512.9,0.99816,0.53857,0.8524,0.66142,0.51457,0.54901,0.34881,1.19059,0.78766,0.88838,0.0007624,0.0007624,0.0007624
146,10585.5,1.00019,0.54106,0.85295,0.66344,0.50904,0.54875,0.34906,1.19109,0.78768,0.88819,0.00076075,0.00076075,0.00076075
147,10658,0.99693,0.53674,0.85209,0.66666,0.50948,0.54895,0.3493,1.18967,0.78689,0.88798,0.0007591,0.0007591,0.0007591
148,10730.4,0.99241,0.53463,0.85142,0.66763,0.50781,0.5486,0.34908,1.19034,0.78767,0.8883,0.00075745,0.00075745,0.00075745
149,10802.9,1.00106,0.53681,0.85242,0.65939,0.51222,0.54899,0.34922,1.19045,0.78775,0.8883,0.0007558,0.0007558,0.0007558
150,10875.3,0.9881,0.52889,0.85046,0.66222,0.51291,0.5489,0.34956,1.19073,0.78744,0.8883,0.00075415,0.00075415,0.00075415
151,10947.8,0.99636,0.53499,0.8503,0.66432,0.51159,0.54878,0.34942,1.19082,0.78792,0.88841,0.0007525,0.0007525,0.0007525
152,11020.4,0.98805,0.52658,0.84947,0.66569,0.51068,0.54909,0.34954,1.19173,0.78772,0.88873,0.00075085,0.00075085,0.00075085
153,11092.9,0.98996,0.5288,0.85036,0.66541,0.50968,0.54912,0.34931,1.19185,0.78791,0.88897,0.0007492,0.0007492,0.0007492
154,11165.3,0.97409,0.51963,0.84792,0.66391,0.50984,0.54895,0.34952,1.1924,0.78811,0.88889,0.00074755,0.00074755,0.00074755
155,11237.8,0.99204,0.53211,0.84892,0.66625,0.50848,0.54913,0.34946,1.19204,0.78816,0.88902,0.0007459,0.0007459,0.0007459
156,11310.4,0.98509,0.52689,0.84924,0.66405,0.51149,0.54916,0.34911,1.19231,0.78956,0.8891,0.00074425,0.00074425,0.00074425
157,11382.7,0.9873,0.52701,0.85052,0.6693,0.50888,0.54924,0.34926,1.19219,0.78972,0.88914,0.0007426,0.0007426,0.0007426
158,11455.2,0.98402,0.52789,0.84837,0.66936,0.50993,0.54938,0.34948,1.19207,0.78941,0.8893,0.00074095,0.00074095,0.00074095
159,11527.8,0.98178,0.52533,0.849,0.66781,0.51034,0.54917,0.34915,1.19219,0.78974,0.88941,0.0007393,0.0007393,0.0007393
160,11600.2,0.98086,0.52384,0.84882,0.66549,0.51197,0.54901,0.34954,1.19206,0.78933,0.88931,0.00073765,0.00073765,0.00073765
161,11672.7,0.98053,0.52369,0.84896,0.67099,0.51082,0.54982,0.34951,1.19141,0.78863,0.88924,0.000736,0.000736,0.000736
162,11745.2,0.97465,0.52035,0.84683,0.66802,0.51244,0.54945,0.34957,1.1915,0.78912,0.8891,0.00073435,0.00073435,0.00073435
163,11817.8,0.98263,0.52287,0.84781,0.67022,0.51189,0.54947,0.3496,1.19144,0.79003,0.88916,0.0007327,0.0007327,0.0007327
164,11890.2,0.97093,0.51706,0.84904,0.6667,0.51444,0.54973,0.3496,1.19176,0.78995,0.88938,0.00073105,0.00073105,0.00073105
165,11962.6,0.97387,0.51707,0.84878,0.66649,0.51356,0.54934,0.34921,1.19142,0.79069,0.88964,0.0007294,0.0007294,0.0007294
166,12035,0.97158,0.51715,0.84706,0.66408,0.51577,0.54935,0.34929,1.19151,0.79211,0.88975,0.00072775,0.00072775,0.00072775
167,12107.4,0.96969,0.51756,0.84623,0.66243,0.51491,0.54937,0.34926,1.19108,0.79261,0.88979,0.0007261,0.0007261,0.0007261
168,12179.7,0.97492,0.52131,0.84708,0.65798,0.51608,0.54866,0.34894,1.19118,0.79303,0.88974,0.00072445,0.00072445,0.00072445
169,12252.1,0.96656,0.51485,0.84465,0.65886,0.5165,0.54871,0.34894,1.19131,0.794,0.88985,0.0007228,0.0007228,0.0007228
170,12324.3,0.97422,0.51749,0.8488,0.65565,0.51773,0.54872,0.34858,1.19165,0.79394,0.88991,0.00072115,0.00072115,0.00072115
171,12396.7,0.96957,0.51808,0.84868,0.65653,0.51837,0.54889,0.34844,1.19213,0.79433,0.89001,0.0007195,0.0007195,0.0007195
172,12469.2,0.96935,0.51438,0.84538,0.65765,0.51836,0.549,0.34826,1.19172,0.79536,0.89,0.00071785,0.00071785,0.00071785
173,12541.6,0.97113,0.5194,0.84726,0.65638,0.51899,0.54894,0.34851,1.19239,0.79557,0.89007,0.0007162,0.0007162,0.0007162
174,12614,0.97074,0.51705,0.8473,0.65649,0.51758,0.54895,0.34835,1.19273,0.79571,0.8902,0.00071455,0.00071455,0.00071455
175,12686.4,0.96092,0.51021,0.84402,0.65739,0.517,0.54871,0.34865,1.19303,0.79583,0.89037,0.0007129,0.0007129,0.0007129
176,12758.9,0.96361,0.50986,0.84593,0.65533,0.51628,0.5486,0.34823,1.19266,0.79628,0.89038,0.00071125,0.00071125,0.00071125
177,12831.2,0.96305,0.51156,0.84572,0.65771,0.51455,0.54867,0.34846,1.19266,0.7964,0.89035,0.0007096,0.0007096,0.0007096
178,12903.7,0.96548,0.51648,0.84695,0.6566,0.51506,0.54853,0.34811,1.19251,0.79628,0.89031,0.00070795,0.00070795,0.00070795
179,12976.2,0.96977,0.51527,0.84652,0.65736,0.51312,0.54842,0.34797,1.19256,0.79713,0.89019,0.0007063,0.0007063,0.0007063
180,13048.7,0.968,0.51154,0.84488,0.65724,0.51276,0.54798,0.34791,1.19222,0.79745,0.89,0.00070465,0.00070465,0.00070465
181,13121.2,0.96426,0.51003,0.8444,0.65641,0.51402,0.54828,0.34772,1.19231,0.79678,0.88996,0.000703,0.000703,0.000703
182,13193.7,0.96249,0.5106,0.84503,0.65582,0.51342,0.54805,0.34761,1.1923,0.79708,0.89005,0.00070135,0.00070135,0.00070135
183,13266.2,0.95701,0.50576,0.84421,0.65401,0.51605,0.5479,0.34767,1.19241,0.79742,0.89001,0.0006997,0.0006997,0.0006997
184,13338.7,0.95766,0.50939,0.84378,0.65337,0.5165,0.54811,0.34764,1.19268,0.79783,0.89003,0.00069805,0.00069805,0.00069805
185,13411.1,0.96173,0.50975,0.84391,0.65658,0.51541,0.5479,0.34748,1.19282,0.79808,0.88989,0.0006964,0.0006964,0.0006964
186,13483.6,0.95881,0.50771,0.84393,0.6541,0.51585,0.54788,0.3476,1.19337,0.79828,0.89004,0.00069475,0.00069475,0.00069475
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 72.737 1.45177 1.3499 0.97119 0.48954 0.30681 0.30529 0.17932 1.46069 1.21004 0.94941 0.0671728 0.000331588 0.000331588
3 2 145.259 1.41643 1.09937 0.95936 0.51569 0.348 0.36711 0.21975 1.40953 1.04933 0.9344 0.0341717 0.000663824 0.000663824
4 3 217.511 1.39519 1.03755 0.94925 0.53215 0.35639 0.37598 0.22147 1.4217 1.01354 0.94558 0.00116948 0.000994961 0.000994961
5 4 290.096 1.38565 1.0174 0.94712 0.53937 0.37549 0.39881 0.23714 1.40609 1.01564 0.93907 0.00099505 0.00099505 0.00099505
6 5 362.608 1.35601 0.97607 0.94142 0.54992 0.39999 0.4189 0.25481 1.364 0.96776 0.9313 0.0009934 0.0009934 0.0009934
7 6 435.063 1.32156 0.93836 0.93301 0.55129 0.41798 0.44074 0.267 1.33671 0.93321 0.92151 0.00099175 0.00099175 0.00099175
8 7 507.477 1.31513 0.92741 0.92914 0.56997 0.41232 0.44804 0.26913 1.33529 0.91844 0.92161 0.0009901 0.0009901 0.0009901
9 8 579.866 1.30385 0.91348 0.9277 0.59221 0.41174 0.44511 0.26816 1.3207 0.89299 0.91879 0.00098845 0.00098845 0.00098845
10 9 652.3 1.29593 0.89772 0.92379 0.57481 0.44673 0.47319 0.28947 1.29252 0.91278 0.91245 0.0009868 0.0009868 0.0009868
11 10 724.753 1.2773 0.88313 0.92125 0.56154 0.43174 0.45954 0.27926 1.32213 0.89067 0.91499 0.00098515 0.00098515 0.00098515
12 11 797.108 1.26686 0.86462 0.91826 0.59164 0.414 0.45505 0.27925 1.32693 0.9093 0.91792 0.0009835 0.0009835 0.0009835
13 12 869.466 1.26241 0.8635 0.91615 0.59304 0.44577 0.4814 0.2946 1.28946 0.86411 0.91064 0.00098185 0.00098185 0.00098185
14 13 941.916 1.25849 0.8488 0.9133 0.59279 0.45301 0.48149 0.29445 1.30078 0.86615 0.90923 0.0009802 0.0009802 0.0009802
15 14 1014.13 1.25146 0.84592 0.91302 0.60702 0.46152 0.49562 0.30434 1.28529 0.8501 0.90949 0.00097855 0.00097855 0.00097855
16 15 1086.57 1.2433 0.82949 0.90907 0.61154 0.45736 0.49543 0.30455 1.27646 0.84349 0.90697 0.0009769 0.0009769 0.0009769
17 16 1158.96 1.23719 0.8262 0.90884 0.60868 0.45495 0.49393 0.30246 1.2691 0.83582 0.9009 0.00097525 0.00097525 0.00097525
18 17 1231.28 1.23246 0.82207 0.90773 0.61176 0.46142 0.50004 0.30986 1.25366 0.84857 0.90132 0.0009736 0.0009736 0.0009736
19 18 1303.62 1.23402 0.81355 0.9065 0.62448 0.458 0.49844 0.31222 1.25743 0.82516 0.89927 0.00097195 0.00097195 0.00097195
20 19 1376 1.21622 0.80395 0.90508 0.60854 0.46524 0.50922 0.31668 1.26839 0.83097 0.9038 0.0009703 0.0009703 0.0009703
21 20 1448.27 1.21996 0.79983 0.90519 0.60858 0.47148 0.50558 0.31409 1.25818 0.82764 0.90293 0.00096865 0.00096865 0.00096865
22 21 1520.61 1.22322 0.80093 0.9052 0.64455 0.46105 0.50956 0.31452 1.25627 0.83241 0.9029 0.000967 0.000967 0.000967
23 22 1593.02 1.20371 0.78634 0.90081 0.59679 0.45568 0.4922 0.30748 1.2614 0.83745 0.90051 0.00096535 0.00096535 0.00096535
24 23 1665.36 1.20261 0.7808 0.90082 0.63045 0.4775 0.51558 0.32076 1.25094 0.81355 0.89995 0.0009637 0.0009637 0.0009637
25 24 1737.64 1.19467 0.77229 0.89903 0.62333 0.46517 0.50437 0.31446 1.23966 0.81092 0.90075 0.00096205 0.00096205 0.00096205
26 25 1810.04 1.19834 0.77569 0.90157 0.63327 0.47282 0.5167 0.32484 1.24061 0.82096 0.89414 0.0009604 0.0009604 0.0009604
27 26 1882.36 1.19423 0.76706 0.90021 0.63337 0.46873 0.52114 0.32483 1.23635 0.81724 0.89706 0.00095875 0.00095875 0.00095875
28 27 1954.69 1.19284 0.76055 0.89767 0.63242 0.48032 0.52245 0.3258 1.25015 0.8098 0.90155 0.0009571 0.0009571 0.0009571
29 28 2027.05 1.19259 0.76047 0.896 0.62508 0.47202 0.51627 0.32041 1.25853 0.81012 0.90283 0.00095545 0.00095545 0.00095545
30 29 2099.39 1.19025 0.75798 0.89554 0.64925 0.48362 0.53221 0.33325 1.22656 0.8042 0.89485 0.0009538 0.0009538 0.0009538
31 30 2171.61 1.18241 0.74459 0.89648 0.64479 0.48298 0.52818 0.3286 1.22912 0.79349 0.89775 0.00095215 0.00095215 0.00095215
32 31 2243.86 1.17875 0.75268 0.89338 0.64261 0.4879 0.53199 0.33534 1.22343 0.79848 0.89628 0.0009505 0.0009505 0.0009505
33 32 2316.19 1.18473 0.75119 0.89432 0.62964 0.48815 0.52537 0.32652 1.22146 0.80032 0.89288 0.00094885 0.00094885 0.00094885
34 33 2388.56 1.17415 0.74142 0.89419 0.65025 0.47264 0.52712 0.32948 1.22089 0.78761 0.89377 0.0009472 0.0009472 0.0009472
35 34 2460.92 1.16609 0.73147 0.89178 0.6414 0.47677 0.52358 0.32527 1.22078 0.79837 0.8932 0.00094555 0.00094555 0.00094555
36 35 2533.91 1.16549 0.73254 0.89103 0.64599 0.48047 0.52853 0.32827 1.2239 0.78803 0.89588 0.0009439 0.0009439 0.0009439
37 36 2608.02 1.1644 0.73027 0.89064 0.64509 0.48353 0.53129 0.33218 1.21224 0.78572 0.89136 0.00094225 0.00094225 0.00094225
38 37 2681.66 1.16125 0.7269 0.88967 0.65259 0.4918 0.53473 0.33366 1.21601 0.78924 0.89365 0.0009406 0.0009406 0.0009406
39 38 2754 1.16903 0.73047 0.89189 0.66162 0.48744 0.53717 0.33566 1.21969 0.78049 0.89523 0.00093895 0.00093895 0.00093895
40 39 2826.34 1.16142 0.71879 0.88806 0.6421 0.48384 0.52918 0.33237 1.21605 0.79474 0.89238 0.0009373 0.0009373 0.0009373
41 40 2898.6 1.15578 0.71459 0.88879 0.65262 0.49704 0.54023 0.34085 1.21222 0.7766 0.894 0.00093565 0.00093565 0.00093565
42 41 2971.03 1.15058 0.71198 0.88627 0.64881 0.49684 0.5465 0.34272 1.21169 0.77826 0.89543 0.000934 0.000934 0.000934
43 42 3043.4 1.15016 0.70886 0.88804 0.66036 0.49255 0.54224 0.3392 1.21453 0.77956 0.89251 0.00093235 0.00093235 0.00093235
44 43 3115.8 1.14813 0.70686 0.88723 0.64109 0.50083 0.54222 0.34065 1.20817 0.77646 0.88969 0.0009307 0.0009307 0.0009307
45 44 3188.07 1.1452 0.70293 0.88504 0.65725 0.49637 0.53884 0.33921 1.21544 0.77561 0.89433 0.00092905 0.00092905 0.00092905
46 45 3260.45 1.14926 0.70372 0.88566 0.65689 0.4884 0.53707 0.3368 1.21143 0.77677 0.89118 0.0009274 0.0009274 0.0009274
47 46 3332.69 1.14623 0.7 0.88575 0.66543 0.48589 0.53812 0.33877 1.21676 0.77225 0.8931 0.00092575 0.00092575 0.00092575
48 47 3405.23 1.13626 0.69402 0.88284 0.65806 0.49123 0.54479 0.34009 1.20989 0.78151 0.89305 0.0009241 0.0009241 0.0009241
49 48 3477.59 1.14177 0.69621 0.88564 0.6595 0.49283 0.54478 0.34213 1.20568 0.78292 0.89117 0.00092245 0.00092245 0.00092245
50 49 3549.97 1.13236 0.68789 0.88352 0.66013 0.48967 0.53958 0.33687 1.21342 0.7733 0.89422 0.0009208 0.0009208 0.0009208
51 50 3622.23 1.13373 0.6825 0.88151 0.66861 0.49138 0.5436 0.34066 1.21085 0.7781 0.89259 0.00091915 0.00091915 0.00091915
52 51 3694.9 1.12987 0.68214 0.88055 0.64976 0.50479 0.54459 0.34273 1.20041 0.76117 0.88906 0.0009175 0.0009175 0.0009175
53 52 3769.1 1.12952 0.68015 0.88236 0.67014 0.49388 0.54785 0.34431 1.20305 0.76721 0.89067 0.00091585 0.00091585 0.00091585
54 53 3845.24 1.12818 0.67503 0.88015 0.65329 0.50391 0.54998 0.34518 1.20071 0.77044 0.88734 0.0009142 0.0009142 0.0009142
55 54 3920.19 1.12539 0.67421 0.87931 0.67196 0.48386 0.54301 0.34231 1.19752 0.77885 0.88972 0.00091255 0.00091255 0.00091255
56 55 3993.34 1.11628 0.66518 0.87976 0.67265 0.50073 0.55034 0.34705 1.19835 0.7641 0.88766 0.0009109 0.0009109 0.0009109
57 56 4065.6 1.11883 0.66422 0.88026 0.66827 0.486 0.54042 0.34098 1.19894 0.77048 0.89036 0.00090925 0.00090925 0.00090925
58 57 4138.08 1.10893 0.65857 0.87715 0.65354 0.49715 0.54175 0.34216 1.20237 0.77624 0.88906 0.0009076 0.0009076 0.0009076
59 58 4210.32 1.11571 0.66504 0.87863 0.65137 0.49786 0.54437 0.3412 1.19606 0.76897 0.88918 0.00090595 0.00090595 0.00090595
60 59 4282.52 1.11779 0.66166 0.87904 0.64097 0.50391 0.5457 0.34429 1.19209 0.76886 0.88617 0.0009043 0.0009043 0.0009043
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vis6class_exp/vis6class_v8m/6class_final/args.yaml
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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8m.pt
data: /home/cuuva/experiment/vis6class_exp/vis6class.yaml
epochs: 600
time: null
patience: 100
batch: -1
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95,6082.01,1.05942,0.59723,0.8664,0.6852,0.50147,0.55346,0.35114,1.13373,0.72241,0.88192,0.0008449,0.0008449,0.0008449
96,6145.9,1.05893,0.59289,0.86423,0.66682,0.50966,0.55433,0.35088,1.13276,0.72234,0.88136,0.00084325,0.00084325,0.00084325
97,6210.04,1.06301,0.59375,0.86454,0.66694,0.50716,0.55264,0.35205,1.13275,0.72377,0.88118,0.0008416,0.0008416,0.0008416
98,6273.91,1.06074,0.59305,0.86506,0.66494,0.50852,0.55146,0.34935,1.13161,0.72461,0.88084,0.00083995,0.00083995,0.00083995
99,6337.73,1.06084,0.59227,0.86463,0.66634,0.51215,0.55356,0.35078,1.13027,0.72471,0.88097,0.0008383,0.0008383,0.0008383
100,6401.55,1.05704,0.59204,0.86543,0.6699,0.51056,0.55416,0.35218,1.13266,0.72449,0.88103,0.00083665,0.00083665,0.00083665
101,6465.41,1.05845,0.59215,0.86499,0.67652,0.50625,0.55434,0.35261,1.13203,0.72237,0.88132,0.000835,0.000835,0.000835
102,6529.24,1.05855,0.59032,0.86438,0.68147,0.50831,0.5554,0.35309,1.13348,0.72366,0.88215,0.00083335,0.00083335,0.00083335
103,6593.34,1.04906,0.58376,0.86221,0.67315,0.51159,0.55654,0.35352,1.13284,0.72488,0.88155,0.0008317,0.0008317,0.0008317
104,6657.43,1.05537,0.58667,0.8631,0.6874,0.50953,0.55661,0.35209,1.13384,0.72472,0.88206,0.00083005,0.00083005,0.00083005
105,6721.32,1.05584,0.58685,0.86302,0.68912,0.50328,0.55256,0.35034,1.13486,0.72571,0.88185,0.0008284,0.0008284,0.0008284
106,6785.3,1.05025,0.58386,0.86156,0.67477,0.51341,0.5544,0.35084,1.13437,0.72376,0.88185,0.00082675,0.00082675,0.00082675
107,6849.13,1.04294,0.57856,0.86364,0.67935,0.51248,0.55402,0.35082,1.13324,0.72233,0.88183,0.0008251,0.0008251,0.0008251
108,6912.99,1.04676,0.57953,0.86253,0.67312,0.51609,0.55647,0.35225,1.13229,0.72598,0.88192,0.00082345,0.00082345,0.00082345
109,6977.08,1.04805,0.58048,0.86251,0.64971,0.52309,0.55442,0.35182,1.13208,0.72577,0.88147,0.0008218,0.0008218,0.0008218
110,7040.97,1.04438,0.5779,0.86216,0.66714,0.51648,0.55406,0.3513,1.13224,0.72763,0.88204,0.00082015,0.00082015,0.00082015
111,7105.05,1.04103,0.572,0.86076,0.68105,0.51288,0.55611,0.35236,1.13281,0.7258,0.88215,0.0008185,0.0008185,0.0008185
112,7168.85,1.04171,0.57668,0.86113,0.66468,0.52007,0.55723,0.3535,1.13323,0.72519,0.88274,0.00081685,0.00081685,0.00081685
113,7232.74,1.03923,0.57297,0.86001,0.66402,0.51804,0.55718,0.35329,1.13378,0.72517,0.88255,0.0008152,0.0008152,0.0008152
114,7296.86,1.03079,0.5682,0.8588,0.67095,0.51772,0.55727,0.35329,1.13437,0.72443,0.88221,0.00081355,0.00081355,0.00081355
115,7360.97,1.03641,0.56569,0.85791,0.67918,0.51061,0.55604,0.35278,1.13426,0.72551,0.88249,0.0008119,0.0008119,0.0008119
116,7424.79,1.03328,0.56741,0.86111,0.6711,0.51433,0.55457,0.35196,1.13681,0.72667,0.88327,0.00081025,0.00081025,0.00081025
117,7488.74,1.03303,0.56748,0.86006,0.67779,0.51406,0.55545,0.35161,1.13704,0.72713,0.88365,0.0008086,0.0008086,0.0008086
118,7552.81,1.03389,0.56918,0.85857,0.66465,0.52047,0.55381,0.34991,1.13708,0.7282,0.88388,0.00080695,0.00080695,0.00080695
119,7616.54,1.03357,0.56887,0.85959,0.66975,0.51673,0.5536,0.35017,1.13596,0.72758,0.88399,0.0008053,0.0008053,0.0008053
120,7680.41,1.02314,0.55949,0.85958,0.67207,0.51184,0.55282,0.34957,1.1357,0.72921,0.88408,0.00080365,0.00080365,0.00080365
121,7744.51,1.03528,0.56551,0.85896,0.66501,0.51603,0.5527,0.34874,1.13506,0.72942,0.88371,0.000802,0.000802,0.000802
122,7808.32,1.02428,0.56044,0.8584,0.66364,0.51467,0.55249,0.34852,1.13462,0.7299,0.8834,0.00080035,0.00080035,0.00080035
123,7872.32,1.02822,0.56344,0.85863,0.65986,0.51624,0.553,0.34945,1.13644,0.73018,0.88363,0.0007987,0.0007987,0.0007987
124,7936.39,1.03015,0.56336,0.85678,0.65655,0.51864,0.55417,0.35048,1.13726,0.73125,0.88362,0.00079705,0.00079705,0.00079705
125,8000.48,1.02651,0.56362,0.8583,0.65915,0.51812,0.55379,0.35069,1.13676,0.73003,0.88364,0.0007954,0.0007954,0.0007954
126,8064.31,1.0297,0.56073,0.85847,0.66272,0.51678,0.55289,0.35062,1.13578,0.72939,0.88339,0.00079375,0.00079375,0.00079375
127,8128.28,1.02396,0.55684,0.85469,0.661,0.51863,0.55149,0.34905,1.13634,0.73048,0.8837,0.0007921,0.0007921,0.0007921
128,8192.06,1.01717,0.55376,0.85612,0.66481,0.51631,0.55126,0.34887,1.13553,0.73112,0.88385,0.00079045,0.00079045,0.00079045
129,8256.38,1.02283,0.55599,0.85613,0.67058,0.51584,0.55172,0.34792,1.13536,0.73156,0.8839,0.0007888,0.0007888,0.0007888
130,8320.32,1.0258,0.55782,0.85678,0.66638,0.51726,0.55244,0.34785,1.13523,0.73115,0.88401,0.00078715,0.00078715,0.00078715
131,8384.46,1.01587,0.55501,0.85619,0.66601,0.51425,0.5522,0.3479,1.13498,0.73134,0.884,0.0007855,0.0007855,0.0007855
132,8448.37,1.01501,0.55109,0.85626,0.66878,0.51446,0.55208,0.34836,1.13563,0.73171,0.88418,0.00078385,0.00078385,0.00078385
133,8512.26,1.01238,0.54966,0.8545,0.67126,0.51412,0.55145,0.34887,1.13602,0.73269,0.88405,0.0007822,0.0007822,0.0007822
134,8576.46,1.01704,0.55273,0.85584,0.68169,0.50635,0.55135,0.34823,1.13514,0.73211,0.88399,0.00078055,0.00078055,0.00078055
135,8640.32,1.01448,0.55054,0.85395,0.67828,0.50893,0.55219,0.34882,1.1356,0.73239,0.88407,0.0007789,0.0007789,0.0007789
136,8704.41,1.01297,0.54947,0.85442,0.68237,0.50502,0.55136,0.34922,1.13583,0.73254,0.8839,0.00077725,0.00077725,0.00077725
137,8768.43,1.01278,0.54742,0.85463,0.67332,0.5096,0.55168,0.34973,1.13671,0.73254,0.88407,0.0007756,0.0007756,0.0007756
138,8832.29,1.01271,0.54791,0.85515,0.6777,0.50789,0.55138,0.34941,1.13641,0.73376,0.88409,0.00077395,0.00077395,0.00077395
139,8896.14,1.00708,0.54649,0.85457,0.68264,0.5068,0.55109,0.34879,1.13625,0.73337,0.88417,0.0007723,0.0007723,0.0007723
140,8959.92,1.00589,0.54721,0.85353,0.68586,0.5059,0.55099,0.34856,1.13684,0.7338,0.88433,0.00077065,0.00077065,0.00077065
141,9023.68,1.01197,0.54497,0.85557,0.68826,0.50547,0.5517,0.34833,1.13624,0.73378,0.88418,0.000769,0.000769,0.000769
142,9087.77,1.00994,0.5461,0.85498,0.6827,0.50763,0.55145,0.34852,1.136,0.73374,0.88404,0.00076735,0.00076735,0.00076735
143,9151.79,1.00489,0.54407,0.85265,0.6722,0.51226,0.55141,0.34962,1.13494,0.73426,0.88372,0.0007657,0.0007657,0.0007657
144,9215.86,1.00291,0.54088,0.85307,0.66429,0.51626,0.55203,0.34981,1.13518,0.73452,0.88378,0.00076405,0.00076405,0.00076405
145,9279.89,1.0037,0.54351,0.85326,0.66512,0.51543,0.55162,0.34945,1.13457,0.73382,0.88359,0.0007624,0.0007624,0.0007624
146,9343.84,1.00555,0.54271,0.85324,0.66167,0.51907,0.55163,0.34959,1.13497,0.73373,0.88385,0.00076075,0.00076075,0.00076075
147,9407.66,0.99876,0.5389,0.85363,0.65881,0.5197,0.55177,0.3492,1.13441,0.7335,0.88366,0.0007591,0.0007591,0.0007591
148,9471.57,0.99579,0.53835,0.85242,0.66106,0.51696,0.55085,0.34901,1.13446,0.73316,0.88357,0.00075745,0.00075745,0.00075745
149,9535.77,1.00651,0.54226,0.85207,0.66219,0.51447,0.55106,0.34892,1.13452,0.73249,0.88349,0.0007558,0.0007558,0.0007558
150,9599.69,1.00348,0.53746,0.85181,0.66458,0.51443,0.55139,0.34975,1.13458,0.73227,0.88355,0.00075415,0.00075415,0.00075415
151,9663.78,0.99815,0.53816,0.85026,0.66469,0.5146,0.55146,0.34974,1.13496,0.7321,0.88361,0.0007525,0.0007525,0.0007525
152,9727.62,0.99518,0.53571,0.85231,0.66481,0.51608,0.55179,0.3496,1.13502,0.73167,0.88374,0.00075085,0.00075085,0.00075085
153,9791.55,0.99609,0.53557,0.85246,0.66681,0.51391,0.55148,0.34929,1.13497,0.73164,0.88389,0.0007492,0.0007492,0.0007492
154,9855.69,0.98625,0.52876,0.84867,0.67153,0.5128,0.5508,0.34908,1.13498,0.73194,0.88399,0.00074755,0.00074755,0.00074755
155,9919.59,0.99132,0.5326,0.85064,0.67047,0.5128,0.55033,0.34838,1.13494,0.73264,0.88404,0.0007459,0.0007459,0.0007459
156,9983.69,0.99157,0.53136,0.85102,0.6721,0.51393,0.55079,0.34831,1.13473,0.73255,0.88403,0.00074425,0.00074425,0.00074425
157,10047.8,0.99016,0.5309,0.8512,0.67463,0.51134,0.55099,0.34806,1.13433,0.73258,0.88378,0.0007426,0.0007426,0.0007426
158,10111.7,0.98982,0.53259,0.85117,0.67612,0.50967,0.5509,0.34767,1.13427,0.73305,0.88375,0.00074095,0.00074095,0.00074095
159,10175.6,0.98865,0.53309,0.84996,0.67514,0.50914,0.55105,0.34823,1.1343,0.73355,0.88382,0.0007393,0.0007393,0.0007393
160,10239.4,0.99202,0.52971,0.85173,0.67624,0.50778,0.55086,0.34831,1.13407,0.73311,0.88381,0.00073765,0.00073765,0.00073765
161,10303.2,0.98676,0.52927,0.85012,0.67593,0.50735,0.55064,0.34804,1.13416,0.73365,0.88386,0.000736,0.000736,0.000736
162,10367,0.98663,0.52832,0.84805,0.675,0.509,0.55026,0.34768,1.13431,0.73386,0.88398,0.00073435,0.00073435,0.00073435
163,10431.1,0.98261,0.52547,0.84916,0.67632,0.50844,0.55082,0.34759,1.13458,0.73427,0.88413,0.0007327,0.0007327,0.0007327
164,10495.1,0.98621,0.52532,0.84968,0.675,0.5103,0.54973,0.34755,1.1346,0.73448,0.8841,0.00073105,0.00073105,0.00073105
165,10559.2,0.98422,0.52592,0.84989,0.67198,0.51332,0.54915,0.34749,1.13463,0.73555,0.88414,0.0007294,0.0007294,0.0007294
166,10623.2,0.98072,0.52351,0.84915,0.67387,0.51222,0.54926,0.34783,1.13492,0.73591,0.88419,0.00072775,0.00072775,0.00072775
167,10687.3,0.97795,0.5218,0.84846,0.6759,0.51134,0.54982,0.34829,1.13551,0.73583,0.88417,0.0007261,0.0007261,0.0007261
168,10751.3,0.98896,0.52968,0.84838,0.6749,0.51194,0.55036,0.34836,1.13566,0.73555,0.88402,0.00072445,0.00072445,0.00072445
169,10815.3,0.97516,0.51849,0.84661,0.67322,0.51221,0.55035,0.34862,1.13581,0.73567,0.88404,0.0007228,0.0007228,0.0007228
170,10879.1,0.98088,0.5221,0.8475,0.67445,0.51068,0.55027,0.34872,1.13608,0.73612,0.88414,0.00072115,0.00072115,0.00072115
171,10943.2,0.98014,0.52431,0.84878,0.67273,0.5111,0.55064,0.3487,1.13588,0.7364,0.88425,0.0007195,0.0007195,0.0007195
172,11007.4,0.97471,0.52028,0.84837,0.67331,0.51107,0.55102,0.3489,1.13588,0.73633,0.88425,0.00071785,0.00071785,0.00071785
173,11071.2,0.9762,0.5198,0.84799,0.67464,0.51164,0.55129,0.34919,1.13599,0.73674,0.88439,0.0007162,0.0007162,0.0007162
174,11135.3,0.97357,0.5202,0.84948,0.67465,0.51303,0.55157,0.34897,1.13609,0.73653,0.88431,0.00071455,0.00071455,0.00071455
175,11199.1,0.97646,0.51946,0.84684,0.67474,0.51296,0.55146,0.34871,1.13585,0.73623,0.88431,0.0007129,0.0007129,0.0007129
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 64.8035 1.44518 1.24871 0.96655 0.46273 0.30975 0.32292 0.19629 1.3705 1.01079 0.9411 0.0671222 0.000332099 0.000332099
3 2 128.733 1.38532 1.05992 0.95003 0.45294 0.35537 0.35387 0.21002 1.35558 0.96973 0.93266 0.0341211 0.000664334 0.000664334
4 3 192.989 1.41881 1.0677 0.956 0.5147 0.36667 0.38396 0.22674 1.35672 0.94898 0.93564 0.00111893 0.00099547 0.00099547
5 4 257.349 1.36225 1.00021 0.94333 0.52509 0.3888 0.40765 0.24078 1.33336 0.92921 0.92681 0.00099505 0.00099505 0.00099505
6 5 321.358 1.33899 0.95993 0.93652 0.53012 0.40704 0.43031 0.2576 1.30196 0.87481 0.91911 0.0009934 0.0009934 0.0009934
7 6 385.205 1.31501 0.93258 0.93075 0.56913 0.41624 0.44554 0.27113 1.2617 0.85588 0.91127 0.00099175 0.00099175 0.00099175
8 7 449.202 1.31006 0.91906 0.92719 0.59437 0.41488 0.45076 0.26983 1.28444 0.85635 0.91591 0.0009901 0.0009901 0.0009901
9 8 513.371 1.30098 0.9078 0.92654 0.56587 0.43995 0.4606 0.27839 1.26495 0.84365 0.91303 0.00098845 0.00098845 0.00098845
10 9 577.554 1.29198 0.89955 0.92354 0.60212 0.43087 0.47296 0.28981 1.23705 0.82191 0.90341 0.0009868 0.0009868 0.0009868
11 10 641.466 1.28778 0.88534 0.92023 0.60386 0.42665 0.46924 0.28664 1.25422 0.83241 0.90805 0.00098515 0.00098515 0.00098515
12 11 705.385 1.26663 0.86475 0.91755 0.62588 0.43605 0.48591 0.29631 1.22575 0.81091 0.90261 0.0009835 0.0009835 0.0009835
13 12 769.296 1.26594 0.85902 0.91545 0.62306 0.43296 0.47924 0.2944 1.22424 0.80609 0.90126 0.00098185 0.00098185 0.00098185
14 13 833.344 1.26118 0.85765 0.91652 0.61108 0.45652 0.49489 0.3044 1.21403 0.80677 0.89726 0.0009802 0.0009802 0.0009802
15 14 897.373 1.25242 0.84303 0.91365 0.59442 0.4403 0.4741 0.28527 1.24185 0.81376 0.90497 0.00097855 0.00097855 0.00097855
16 15 961.466 1.24375 0.83135 0.91025 0.59575 0.45481 0.48823 0.29908 1.22677 0.79404 0.90098 0.0009769 0.0009769 0.0009769
17 16 1025.58 1.2372 0.83359 0.91098 0.59561 0.45908 0.48959 0.29972 1.22976 0.79903 0.90101 0.00097525 0.00097525 0.00097525
18 17 1089.64 1.23689 0.82411 0.90922 0.61181 0.45519 0.49806 0.31014 1.21244 0.78897 0.89719 0.0009736 0.0009736 0.0009736
19 18 1153.66 1.23348 0.81783 0.90767 0.60759 0.47299 0.50648 0.31392 1.19801 0.77305 0.89388 0.00097195 0.00097195 0.00097195
20 19 1217.91 1.22053 0.80689 0.90553 0.63232 0.4683 0.50972 0.31608 1.19722 0.77999 0.89444 0.0009703 0.0009703 0.0009703
21 20 1281.91 1.21907 0.7992 0.90579 0.61865 0.46849 0.50669 0.31537 1.19538 0.77569 0.89383 0.00096865 0.00096865 0.00096865
22 21 1346 1.22905 0.8055 0.90632 0.61836 0.48083 0.51578 0.32058 1.18471 0.76918 0.88998 0.000967 0.000967 0.000967
23 22 1410.13 1.21232 0.79165 0.90136 0.64282 0.46692 0.51349 0.32166 1.19824 0.77388 0.89219 0.00096535 0.00096535 0.00096535
24 23 1474.04 1.20831 0.78677 0.90012 0.61244 0.47925 0.51026 0.32067 1.1791 0.7646 0.88943 0.0009637 0.0009637 0.0009637
25 24 1538.2 1.20848 0.78101 0.89986 0.63258 0.47906 0.51905 0.32194 1.19256 0.77018 0.89139 0.00096205 0.00096205 0.00096205
26 25 1602.18 1.19834 0.77934 0.89891 0.61344 0.48259 0.51653 0.32085 1.1845 0.76663 0.89126 0.0009604 0.0009604 0.0009604
27 26 1666.24 1.20063 0.77599 0.89973 0.62179 0.47035 0.50913 0.32007 1.17346 0.76121 0.8888 0.00095875 0.00095875 0.00095875
28 27 1730.08 1.19376 0.76545 0.89727 0.63575 0.47863 0.52479 0.32763 1.17068 0.75212 0.88685 0.0009571 0.0009571 0.0009571
29 28 1794.03 1.18805 0.76469 0.89782 0.63668 0.47719 0.52512 0.3255 1.18985 0.76704 0.89239 0.00095545 0.00095545 0.00095545
30 29 1857.95 1.18989 0.765 0.89541 0.62915 0.4853 0.52832 0.33179 1.16844 0.74669 0.88637 0.0009538 0.0009538 0.0009538
31 30 1921.79 1.18171 0.75651 0.8955 0.63057 0.47255 0.51411 0.32262 1.16901 0.75514 0.88692 0.00095215 0.00095215 0.00095215
32 31 1985.71 1.18705 0.75535 0.89317 0.65119 0.47569 0.52727 0.32381 1.17397 0.74966 0.88879 0.0009505 0.0009505 0.0009505
33 32 2049.54 1.18147 0.74567 0.89288 0.62949 0.48971 0.52612 0.33159 1.16884 0.74622 0.88706 0.00094885 0.00094885 0.00094885
34 33 2113.65 1.16978 0.73852 0.89109 0.64428 0.47537 0.52524 0.32652 1.16018 0.7442 0.88446 0.0009472 0.0009472 0.0009472
35 34 2177.42 1.16995 0.73718 0.89148 0.63579 0.48866 0.53245 0.33599 1.15473 0.73998 0.88402 0.00094555 0.00094555 0.00094555
36 35 2241.33 1.17518 0.73971 0.89094 0.65465 0.48555 0.53581 0.33659 1.16395 0.74569 0.88555 0.0009439 0.0009439 0.0009439
37 36 2305.47 1.17048 0.73297 0.8924 0.65071 0.48114 0.53187 0.33396 1.16779 0.74155 0.88597 0.00094225 0.00094225 0.00094225
38 37 2369.59 1.17018 0.73188 0.89036 0.64599 0.4981 0.53902 0.33702 1.15918 0.73711 0.88496 0.0009406 0.0009406 0.0009406
39 38 2433.43 1.16699 0.72944 0.89132 0.64679 0.48783 0.53388 0.33523 1.1555 0.73802 0.88384 0.00093895 0.00093895 0.00093895
40 39 2497.58 1.16514 0.72808 0.88999 0.63368 0.49484 0.53736 0.33742 1.16042 0.7363 0.88373 0.0009373 0.0009373 0.0009373
41 40 2561.57 1.16183 0.7236 0.88981 0.6531 0.48973 0.53759 0.33943 1.15121 0.72778 0.88335 0.00093565 0.00093565 0.00093565
42 41 2625.73 1.1525 0.71356 0.88643 0.64971 0.49286 0.541 0.33899 1.15809 0.73146 0.88337 0.000934 0.000934 0.000934
43 42 2689.81 1.16028 0.71917 0.88767 0.67032 0.48375 0.5427 0.34212 1.15283 0.72921 0.88257 0.00093235 0.00093235 0.00093235
44 43 2753.73 1.15168 0.71054 0.8874 0.64121 0.48447 0.53189 0.33567 1.14694 0.73498 0.88224 0.0009307 0.0009307 0.0009307
45 44 2817.52 1.15014 0.70671 0.88659 0.67361 0.4827 0.53749 0.34003 1.1481 0.7262 0.88326 0.00092905 0.00092905 0.00092905
46 45 2881.37 1.14727 0.70715 0.88606 0.66705 0.49305 0.5469 0.34298 1.15 0.72862 0.88311 0.0009274 0.0009274 0.0009274
47 46 2945.31 1.15125 0.70263 0.8862 0.65777 0.49505 0.54361 0.34189 1.14581 0.72585 0.88264 0.00092575 0.00092575 0.00092575
48 47 3009.27 1.14535 0.69739 0.88502 0.65319 0.49376 0.5386 0.33972 1.14944 0.7301 0.88347 0.0009241 0.0009241 0.0009241
49 48 3073.15 1.14672 0.69879 0.88624 0.65801 0.49213 0.54074 0.34253 1.14168 0.7217 0.8813 0.00092245 0.00092245 0.00092245
50 49 3137.08 1.13833 0.69601 0.88406 0.6831 0.48304 0.54416 0.34159 1.1445 0.72569 0.88159 0.0009208 0.0009208 0.0009208
51 50 3201.04 1.13329 0.68679 0.88318 0.67247 0.49799 0.54747 0.34572 1.14426 0.72013 0.88211 0.00091915 0.00091915 0.00091915
52 51 3264.99 1.13471 0.68751 0.8815 0.64192 0.50293 0.54212 0.3426 1.14077 0.72725 0.88076 0.0009175 0.0009175 0.0009175
53 52 3328.91 1.13095 0.68484 0.88112 0.64944 0.50383 0.54335 0.34207 1.14459 0.72942 0.88187 0.00091585 0.00091585 0.00091585
54 53 3392.92 1.13338 0.68141 0.88166 0.66191 0.49664 0.5454 0.34356 1.1425 0.72184 0.88272 0.0009142 0.0009142 0.0009142
55 54 3457.21 1.12874 0.67776 0.88055 0.6693 0.50191 0.54906 0.34847 1.1399 0.72244 0.881 0.00091255 0.00091255 0.00091255
56 55 3521.2 1.12667 0.67808 0.88172 0.66195 0.50304 0.54449 0.34522 1.14364 0.7225 0.88014 0.0009109 0.0009109 0.0009109
57 56 3585.24 1.12688 0.67354 0.88167 0.66481 0.49133 0.54145 0.34078 1.14103 0.71977 0.88097 0.00090925 0.00090925 0.00090925
58 57 3649.13 1.12425 0.66795 0.8794 0.67781 0.48963 0.54656 0.34558 1.13977 0.72294 0.87996 0.0009076 0.0009076 0.0009076
59 58 3713.36 1.11582 0.66389 0.87772 0.66708 0.50037 0.54724 0.34554 1.14188 0.72441 0.88116 0.00090595 0.00090595 0.00090595
60 59 3777.38 1.12489 0.67057 0.88185 0.67666 0.49768 0.54776 0.3485 1.13654 0.72016 0.87997 0.0009043 0.0009043 0.0009043
61 60 3841.56 1.11509 0.6632 0.87877 0.65721 0.50707 0.54991 0.34861 1.13682 0.72234 0.87989 0.00090265 0.00090265 0.00090265
62 61 3905.67 1.11431 0.6636 0.87926 0.66807 0.5013 0.5489 0.34585 1.13701 0.72071 0.87988 0.000901 0.000901 0.000901
63 62 3969.6 1.11513 0.65643 0.87666 0.65831 0.50355 0.54546 0.34632 1.13513 0.71827 0.87947 0.00089935 0.00089935 0.00089935
64 63 4033.5 1.12089 0.66283 0.87836 0.65734 0.49676 0.54185 0.344 1.13705 0.72326 0.87983 0.0008977 0.0008977 0.0008977
65 64 4097.4 1.11201 0.65618 0.87594 0.66989 0.49881 0.54563 0.34639 1.13791 0.72075 0.88058 0.00089605 0.00089605 0.00089605
66 65 4161.29 1.11211 0.65779 0.87727 0.66857 0.50234 0.54927 0.34727 1.13659 0.71555 0.88045 0.0008944 0.0008944 0.0008944
67 66 4225.47 1.11522 0.65243 0.87697 0.66791 0.50309 0.55517 0.35026 1.13522 0.71725 0.87946 0.00089275 0.00089275 0.00089275
68 67 4289.54 1.10976 0.65186 0.8775 0.66748 0.50032 0.54999 0.34741 1.1305 0.71752 0.87916 0.0008911 0.0008911 0.0008911
69 68 4353.69 1.10584 0.64679 0.87566 0.66842 0.5073 0.55186 0.34668 1.13592 0.71847 0.87917 0.00088945 0.00088945 0.00088945
70 69 4417.64 1.09867 0.64043 0.87553 0.67337 0.50941 0.55703 0.35388 1.12921 0.71507 0.87929 0.0008878 0.0008878 0.0008878
71 70 4481.6 1.10444 0.6455 0.87573 0.66762 0.50716 0.55429 0.35244 1.13469 0.71804 0.88071 0.00088615 0.00088615 0.00088615
72 71 4545.55 1.09745 0.63946 0.87383 0.68035 0.50724 0.55708 0.35418 1.12982 0.71907 0.87833 0.0008845 0.0008845 0.0008845
73 72 4609.77 1.10444 0.645 0.87366 0.66764 0.50861 0.55693 0.35152 1.13669 0.71818 0.87958 0.00088285 0.00088285 0.00088285
74 73 4673.69 1.0917 0.6343 0.87172 0.67272 0.50578 0.55328 0.34915 1.13583 0.72413 0.87934 0.0008812 0.0008812 0.0008812
75 74 4737.63 1.09022 0.62993 0.87131 0.67156 0.50179 0.5534 0.35196 1.13051 0.71811 0.87903 0.00087955 0.00087955 0.00087955
76 75 4801.46 1.08989 0.62971 0.87234 0.65897 0.51301 0.55478 0.35424 1.13245 0.71703 0.87879 0.0008779 0.0008779 0.0008779
77 76 4865.63 1.09852 0.63366 0.87418 0.655 0.51138 0.55247 0.35138 1.13346 0.72068 0.87948 0.00087625 0.00087625 0.00087625
78 77 4929.52 1.08843 0.62825 0.87022 0.67329 0.50409 0.55324 0.35166 1.12838 0.72232 0.87899 0.0008746 0.0008746 0.0008746
79 78 4993.39 1.08913 0.62332 0.87072 0.68063 0.50358 0.55446 0.35042 1.13237 0.72265 0.87992 0.00087295 0.00087295 0.00087295
80 79 5057.52 1.0887 0.62448 0.8701 0.68301 0.5032 0.55435 0.35013 1.13164 0.71981 0.88062 0.0008713 0.0008713 0.0008713
81 80 5121.71 1.08269 0.61771 0.86959 0.67308 0.50434 0.55055 0.34776 1.13235 0.72501 0.8799 0.00086965 0.00086965 0.00086965
82 81 5185.64 1.07984 0.61672 0.8701 0.67472 0.51399 0.55649 0.35101 1.13306 0.72164 0.88022 0.000868 0.000868 0.000868
83 82 5249.68 1.07391 0.61233 0.86904 0.67007 0.5117 0.55377 0.34889 1.13213 0.71981 0.8799 0.00086635 0.00086635 0.00086635
84 83 5313.64 1.0915 0.62505 0.87071 0.67053 0.51382 0.5542 0.3518 1.13241 0.71669 0.88015 0.0008647 0.0008647 0.0008647
85 84 5377.76 1.08274 0.61858 0.86924 0.67007 0.51492 0.554 0.35063 1.12959 0.72378 0.87993 0.00086305 0.00086305 0.00086305
86 85 5441.59 1.0703 0.60979 0.86879 0.67028 0.50742 0.55333 0.34934 1.13234 0.72197 0.87962 0.0008614 0.0008614 0.0008614
87 86 5505.63 1.077 0.61056 0.86852 0.65932 0.51801 0.55319 0.34892 1.1292 0.7205 0.87976 0.00085975 0.00085975 0.00085975
88 87 5569.71 1.07485 0.61264 0.86836 0.66481 0.51436 0.55293 0.35019 1.1314 0.71981 0.87986 0.0008581 0.0008581 0.0008581
89 88 5633.51 1.06894 0.60671 0.86749 0.67424 0.50364 0.55369 0.35028 1.12878 0.71955 0.87965 0.00085645 0.00085645 0.00085645
90 89 5697.43 1.07399 0.6084 0.86845 0.67186 0.50223 0.55047 0.34952 1.13167 0.72561 0.8798 0.0008548 0.0008548 0.0008548
91 90 5761.53 1.06952 0.60621 0.8688 0.65714 0.51069 0.54978 0.34836 1.13385 0.72643 0.88086 0.00085315 0.00085315 0.00085315
92 91 5825.73 1.0756 0.60874 0.86808 0.67593 0.50725 0.55125 0.34984 1.13306 0.72452 0.88113 0.0008515 0.0008515 0.0008515
93 92 5889.74 1.07183 0.6045 0.86655 0.67137 0.50689 0.55205 0.34957 1.13212 0.7247 0.88026 0.00084985 0.00084985 0.00084985
94 93 5953.77 1.06909 0.60325 0.86693 0.6606 0.50999 0.5511 0.34813 1.1307 0.72477 0.88009 0.0008482 0.0008482 0.0008482
95 94 6017.98 1.06868 0.59819 0.8659 0.68322 0.49889 0.55086 0.34968 1.13179 0.72591 0.88046 0.00084655 0.00084655 0.00084655
96 95 6082.01 1.05942 0.59723 0.8664 0.6852 0.50147 0.55346 0.35114 1.13373 0.72241 0.88192 0.0008449 0.0008449 0.0008449
97 96 6145.9 1.05893 0.59289 0.86423 0.66682 0.50966 0.55433 0.35088 1.13276 0.72234 0.88136 0.00084325 0.00084325 0.00084325
98 97 6210.04 1.06301 0.59375 0.86454 0.66694 0.50716 0.55264 0.35205 1.13275 0.72377 0.88118 0.0008416 0.0008416 0.0008416
99 98 6273.91 1.06074 0.59305 0.86506 0.66494 0.50852 0.55146 0.34935 1.13161 0.72461 0.88084 0.00083995 0.00083995 0.00083995
100 99 6337.73 1.06084 0.59227 0.86463 0.66634 0.51215 0.55356 0.35078 1.13027 0.72471 0.88097 0.0008383 0.0008383 0.0008383
101 100 6401.55 1.05704 0.59204 0.86543 0.6699 0.51056 0.55416 0.35218 1.13266 0.72449 0.88103 0.00083665 0.00083665 0.00083665
102 101 6465.41 1.05845 0.59215 0.86499 0.67652 0.50625 0.55434 0.35261 1.13203 0.72237 0.88132 0.000835 0.000835 0.000835
103 102 6529.24 1.05855 0.59032 0.86438 0.68147 0.50831 0.5554 0.35309 1.13348 0.72366 0.88215 0.00083335 0.00083335 0.00083335
104 103 6593.34 1.04906 0.58376 0.86221 0.67315 0.51159 0.55654 0.35352 1.13284 0.72488 0.88155 0.0008317 0.0008317 0.0008317
105 104 6657.43 1.05537 0.58667 0.8631 0.6874 0.50953 0.55661 0.35209 1.13384 0.72472 0.88206 0.00083005 0.00083005 0.00083005
106 105 6721.32 1.05584 0.58685 0.86302 0.68912 0.50328 0.55256 0.35034 1.13486 0.72571 0.88185 0.0008284 0.0008284 0.0008284
107 106 6785.3 1.05025 0.58386 0.86156 0.67477 0.51341 0.5544 0.35084 1.13437 0.72376 0.88185 0.00082675 0.00082675 0.00082675
108 107 6849.13 1.04294 0.57856 0.86364 0.67935 0.51248 0.55402 0.35082 1.13324 0.72233 0.88183 0.0008251 0.0008251 0.0008251
109 108 6912.99 1.04676 0.57953 0.86253 0.67312 0.51609 0.55647 0.35225 1.13229 0.72598 0.88192 0.00082345 0.00082345 0.00082345
110 109 6977.08 1.04805 0.58048 0.86251 0.64971 0.52309 0.55442 0.35182 1.13208 0.72577 0.88147 0.0008218 0.0008218 0.0008218
111 110 7040.97 1.04438 0.5779 0.86216 0.66714 0.51648 0.55406 0.3513 1.13224 0.72763 0.88204 0.00082015 0.00082015 0.00082015
112 111 7105.05 1.04103 0.572 0.86076 0.68105 0.51288 0.55611 0.35236 1.13281 0.7258 0.88215 0.0008185 0.0008185 0.0008185
113 112 7168.85 1.04171 0.57668 0.86113 0.66468 0.52007 0.55723 0.3535 1.13323 0.72519 0.88274 0.00081685 0.00081685 0.00081685
114 113 7232.74 1.03923 0.57297 0.86001 0.66402 0.51804 0.55718 0.35329 1.13378 0.72517 0.88255 0.0008152 0.0008152 0.0008152
115 114 7296.86 1.03079 0.5682 0.8588 0.67095 0.51772 0.55727 0.35329 1.13437 0.72443 0.88221 0.00081355 0.00081355 0.00081355
116 115 7360.97 1.03641 0.56569 0.85791 0.67918 0.51061 0.55604 0.35278 1.13426 0.72551 0.88249 0.0008119 0.0008119 0.0008119
117 116 7424.79 1.03328 0.56741 0.86111 0.6711 0.51433 0.55457 0.35196 1.13681 0.72667 0.88327 0.00081025 0.00081025 0.00081025
118 117 7488.74 1.03303 0.56748 0.86006 0.67779 0.51406 0.55545 0.35161 1.13704 0.72713 0.88365 0.0008086 0.0008086 0.0008086
119 118 7552.81 1.03389 0.56918 0.85857 0.66465 0.52047 0.55381 0.34991 1.13708 0.7282 0.88388 0.00080695 0.00080695 0.00080695
120 119 7616.54 1.03357 0.56887 0.85959 0.66975 0.51673 0.5536 0.35017 1.13596 0.72758 0.88399 0.0008053 0.0008053 0.0008053
121 120 7680.41 1.02314 0.55949 0.85958 0.67207 0.51184 0.55282 0.34957 1.1357 0.72921 0.88408 0.00080365 0.00080365 0.00080365
122 121 7744.51 1.03528 0.56551 0.85896 0.66501 0.51603 0.5527 0.34874 1.13506 0.72942 0.88371 0.000802 0.000802 0.000802
123 122 7808.32 1.02428 0.56044 0.8584 0.66364 0.51467 0.55249 0.34852 1.13462 0.7299 0.8834 0.00080035 0.00080035 0.00080035
124 123 7872.32 1.02822 0.56344 0.85863 0.65986 0.51624 0.553 0.34945 1.13644 0.73018 0.88363 0.0007987 0.0007987 0.0007987
125 124 7936.39 1.03015 0.56336 0.85678 0.65655 0.51864 0.55417 0.35048 1.13726 0.73125 0.88362 0.00079705 0.00079705 0.00079705
126 125 8000.48 1.02651 0.56362 0.8583 0.65915 0.51812 0.55379 0.35069 1.13676 0.73003 0.88364 0.0007954 0.0007954 0.0007954
127 126 8064.31 1.0297 0.56073 0.85847 0.66272 0.51678 0.55289 0.35062 1.13578 0.72939 0.88339 0.00079375 0.00079375 0.00079375
128 127 8128.28 1.02396 0.55684 0.85469 0.661 0.51863 0.55149 0.34905 1.13634 0.73048 0.8837 0.0007921 0.0007921 0.0007921
129 128 8192.06 1.01717 0.55376 0.85612 0.66481 0.51631 0.55126 0.34887 1.13553 0.73112 0.88385 0.00079045 0.00079045 0.00079045
130 129 8256.38 1.02283 0.55599 0.85613 0.67058 0.51584 0.55172 0.34792 1.13536 0.73156 0.8839 0.0007888 0.0007888 0.0007888
131 130 8320.32 1.0258 0.55782 0.85678 0.66638 0.51726 0.55244 0.34785 1.13523 0.73115 0.88401 0.00078715 0.00078715 0.00078715
132 131 8384.46 1.01587 0.55501 0.85619 0.66601 0.51425 0.5522 0.3479 1.13498 0.73134 0.884 0.0007855 0.0007855 0.0007855
133 132 8448.37 1.01501 0.55109 0.85626 0.66878 0.51446 0.55208 0.34836 1.13563 0.73171 0.88418 0.00078385 0.00078385 0.00078385
134 133 8512.26 1.01238 0.54966 0.8545 0.67126 0.51412 0.55145 0.34887 1.13602 0.73269 0.88405 0.0007822 0.0007822 0.0007822
135 134 8576.46 1.01704 0.55273 0.85584 0.68169 0.50635 0.55135 0.34823 1.13514 0.73211 0.88399 0.00078055 0.00078055 0.00078055
136 135 8640.32 1.01448 0.55054 0.85395 0.67828 0.50893 0.55219 0.34882 1.1356 0.73239 0.88407 0.0007789 0.0007789 0.0007789
137 136 8704.41 1.01297 0.54947 0.85442 0.68237 0.50502 0.55136 0.34922 1.13583 0.73254 0.8839 0.00077725 0.00077725 0.00077725
138 137 8768.43 1.01278 0.54742 0.85463 0.67332 0.5096 0.55168 0.34973 1.13671 0.73254 0.88407 0.0007756 0.0007756 0.0007756
139 138 8832.29 1.01271 0.54791 0.85515 0.6777 0.50789 0.55138 0.34941 1.13641 0.73376 0.88409 0.00077395 0.00077395 0.00077395
140 139 8896.14 1.00708 0.54649 0.85457 0.68264 0.5068 0.55109 0.34879 1.13625 0.73337 0.88417 0.0007723 0.0007723 0.0007723
141 140 8959.92 1.00589 0.54721 0.85353 0.68586 0.5059 0.55099 0.34856 1.13684 0.7338 0.88433 0.00077065 0.00077065 0.00077065
142 141 9023.68 1.01197 0.54497 0.85557 0.68826 0.50547 0.5517 0.34833 1.13624 0.73378 0.88418 0.000769 0.000769 0.000769
143 142 9087.77 1.00994 0.5461 0.85498 0.6827 0.50763 0.55145 0.34852 1.136 0.73374 0.88404 0.00076735 0.00076735 0.00076735
144 143 9151.79 1.00489 0.54407 0.85265 0.6722 0.51226 0.55141 0.34962 1.13494 0.73426 0.88372 0.0007657 0.0007657 0.0007657
145 144 9215.86 1.00291 0.54088 0.85307 0.66429 0.51626 0.55203 0.34981 1.13518 0.73452 0.88378 0.00076405 0.00076405 0.00076405
146 145 9279.89 1.0037 0.54351 0.85326 0.66512 0.51543 0.55162 0.34945 1.13457 0.73382 0.88359 0.0007624 0.0007624 0.0007624
147 146 9343.84 1.00555 0.54271 0.85324 0.66167 0.51907 0.55163 0.34959 1.13497 0.73373 0.88385 0.00076075 0.00076075 0.00076075
148 147 9407.66 0.99876 0.5389 0.85363 0.65881 0.5197 0.55177 0.3492 1.13441 0.7335 0.88366 0.0007591 0.0007591 0.0007591
149 148 9471.57 0.99579 0.53835 0.85242 0.66106 0.51696 0.55085 0.34901 1.13446 0.73316 0.88357 0.00075745 0.00075745 0.00075745
150 149 9535.77 1.00651 0.54226 0.85207 0.66219 0.51447 0.55106 0.34892 1.13452 0.73249 0.88349 0.0007558 0.0007558 0.0007558
151 150 9599.69 1.00348 0.53746 0.85181 0.66458 0.51443 0.55139 0.34975 1.13458 0.73227 0.88355 0.00075415 0.00075415 0.00075415
152 151 9663.78 0.99815 0.53816 0.85026 0.66469 0.5146 0.55146 0.34974 1.13496 0.7321 0.88361 0.0007525 0.0007525 0.0007525
153 152 9727.62 0.99518 0.53571 0.85231 0.66481 0.51608 0.55179 0.3496 1.13502 0.73167 0.88374 0.00075085 0.00075085 0.00075085
154 153 9791.55 0.99609 0.53557 0.85246 0.66681 0.51391 0.55148 0.34929 1.13497 0.73164 0.88389 0.0007492 0.0007492 0.0007492
155 154 9855.69 0.98625 0.52876 0.84867 0.67153 0.5128 0.5508 0.34908 1.13498 0.73194 0.88399 0.00074755 0.00074755 0.00074755
156 155 9919.59 0.99132 0.5326 0.85064 0.67047 0.5128 0.55033 0.34838 1.13494 0.73264 0.88404 0.0007459 0.0007459 0.0007459
157 156 9983.69 0.99157 0.53136 0.85102 0.6721 0.51393 0.55079 0.34831 1.13473 0.73255 0.88403 0.00074425 0.00074425 0.00074425
158 157 10047.8 0.99016 0.5309 0.8512 0.67463 0.51134 0.55099 0.34806 1.13433 0.73258 0.88378 0.0007426 0.0007426 0.0007426
159 158 10111.7 0.98982 0.53259 0.85117 0.67612 0.50967 0.5509 0.34767 1.13427 0.73305 0.88375 0.00074095 0.00074095 0.00074095
160 159 10175.6 0.98865 0.53309 0.84996 0.67514 0.50914 0.55105 0.34823 1.1343 0.73355 0.88382 0.0007393 0.0007393 0.0007393
161 160 10239.4 0.99202 0.52971 0.85173 0.67624 0.50778 0.55086 0.34831 1.13407 0.73311 0.88381 0.00073765 0.00073765 0.00073765
162 161 10303.2 0.98676 0.52927 0.85012 0.67593 0.50735 0.55064 0.34804 1.13416 0.73365 0.88386 0.000736 0.000736 0.000736
163 162 10367 0.98663 0.52832 0.84805 0.675 0.509 0.55026 0.34768 1.13431 0.73386 0.88398 0.00073435 0.00073435 0.00073435
164 163 10431.1 0.98261 0.52547 0.84916 0.67632 0.50844 0.55082 0.34759 1.13458 0.73427 0.88413 0.0007327 0.0007327 0.0007327
165 164 10495.1 0.98621 0.52532 0.84968 0.675 0.5103 0.54973 0.34755 1.1346 0.73448 0.8841 0.00073105 0.00073105 0.00073105
166 165 10559.2 0.98422 0.52592 0.84989 0.67198 0.51332 0.54915 0.34749 1.13463 0.73555 0.88414 0.0007294 0.0007294 0.0007294
167 166 10623.2 0.98072 0.52351 0.84915 0.67387 0.51222 0.54926 0.34783 1.13492 0.73591 0.88419 0.00072775 0.00072775 0.00072775
168 167 10687.3 0.97795 0.5218 0.84846 0.6759 0.51134 0.54982 0.34829 1.13551 0.73583 0.88417 0.0007261 0.0007261 0.0007261
169 168 10751.3 0.98896 0.52968 0.84838 0.6749 0.51194 0.55036 0.34836 1.13566 0.73555 0.88402 0.00072445 0.00072445 0.00072445
170 169 10815.3 0.97516 0.51849 0.84661 0.67322 0.51221 0.55035 0.34862 1.13581 0.73567 0.88404 0.0007228 0.0007228 0.0007228
171 170 10879.1 0.98088 0.5221 0.8475 0.67445 0.51068 0.55027 0.34872 1.13608 0.73612 0.88414 0.00072115 0.00072115 0.00072115
172 171 10943.2 0.98014 0.52431 0.84878 0.67273 0.5111 0.55064 0.3487 1.13588 0.7364 0.88425 0.0007195 0.0007195 0.0007195
173 172 11007.4 0.97471 0.52028 0.84837 0.67331 0.51107 0.55102 0.3489 1.13588 0.73633 0.88425 0.00071785 0.00071785 0.00071785
174 173 11071.2 0.9762 0.5198 0.84799 0.67464 0.51164 0.55129 0.34919 1.13599 0.73674 0.88439 0.0007162 0.0007162 0.0007162
175 174 11135.3 0.97357 0.5202 0.84948 0.67465 0.51303 0.55157 0.34897 1.13609 0.73653 0.88431 0.00071455 0.00071455 0.00071455
176 175 11199.1 0.97646 0.51946 0.84684 0.67474 0.51296 0.55146 0.34871 1.13585 0.73623 0.88431 0.0007129 0.0007129 0.0007129
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