{ "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", "πŸ” Checking split: train (files: 6471)\n", "\n", "πŸ” Checking split: val (files: 548)\n", "\n", "πŸ” Checking split: test (files: 1610)\n", "\n", "πŸ“Œ 클래슀 톡계 κ²°κ³Ό:\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 클래슀 μ’…λ₯˜: 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\"\\nπŸ” 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 쀄 제거\n", " class_id = line.split()[0] # 첫번째 κ°’ = 클래슀\n", " class_counter[class_id] += 1\n", "\n", "# μ΅œμ’… κ²°κ³Ό 좜λ ₯\n", "print(\"\\nπŸ“Œ 클래슀 톡계 κ²°κ³Ό:\")\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 클래슀 μ’…λ₯˜: {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 πŸ˜ƒ Update with 'pip install -U ultralytics'\n", "Ultralytics 8.3.225 πŸš€ 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 βœ…\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access βœ… (ping: 0.0Β±0.0 ms, read: 12140.0Β±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%) βœ…\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access βœ… (ping: 0.0Β±0.0 ms, read: 12817.4Β±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 βœ… (ping: 0.0Β±0.0 ms, read: 7032.9Β±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 πŸš€ 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 πŸš€ 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 βœ… 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 }