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1347 lines
113 KiB
1347 lines
113 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a7676704",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ultralytics import YOLO\n",
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"\n",
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3e94066a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"torch.cuda.is_available()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "99b0442c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt': 100% ━━━━━━━━━━━━ 6.2MB 80.2MB/s 0.1s\n",
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"New https://pypi.org/project/ultralytics/8.3.232 available 😃 Update with 'pip install -U ultralytics'\n",
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"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
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"\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/swoon_detect/swoon_detect.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=5, 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=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=test, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=2, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=sw_detect, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/swoon_detect/sw_detect/test, 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",
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"Overriding model.yaml nc=80 with nc=1\n",
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"\n",
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" from n params module arguments \n",
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" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
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" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
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" 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n",
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" 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
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" 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n",
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" 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
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" 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
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" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
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" 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n",
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" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
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" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
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" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
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" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
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" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
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" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
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" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
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" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
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" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
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" 22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] \n",
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"Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs\n",
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"\n",
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"Transferred 319/355 items from pretrained weights\n",
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"Freezing layer 'model.22.dfl.conv.weight'\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
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"\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 69.1MB/s 0.1s\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 10741.3±8674.9 MB/s, size: 3138.0 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01... 1294 images, 101 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1395/1395 8.9Kit/s 0.2ss\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01.cache\n",
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"\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",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 0.15G reserved, 0.06G allocated, 31.12G free\n",
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" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
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" 3011043 8.194 3.456 28.52 221.4 (1, 3, 640, 640) list\n",
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" 3011043 16.39 3.999 4.5 24.42 (2, 3, 640, 640) list\n",
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" 3011043 32.78 4.272 5.119 24.48 (4, 3, 640, 640) list\n",
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" 3011043 65.55 4.983 5.584 30.13 (8, 3, 640, 640) list\n",
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" 3011043 131.1 6.126 7.802 40.59 (16, 3, 640, 640) list\n",
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" 3011043 262.2 4.863 15.04 54.22 (32, 3, 640, 640) list\n",
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" 3011043 524.4 10.561 30.92 104.5 (64, 3, 640, 640) list\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 141 for CUDA:0 19.16G/31.33G (61%) ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 13251.1±2708.2 MB/s, size: 3604.8 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01.cache... 1294 images, 101 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1395/1395 4.9Mit/s 0.0s0s\n",
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"\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",
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"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 7220.3±4763.2 MB/s, size: 3721.2 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Validation/labels/03... 553 images, 35 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 588/588 6.9Kit/s 0.1s\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/swoon_detection/Validation/labels/03.cache\n",
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"Plotting labels to /home/cuuva/experiment/swoon_detect/sw_detect/test/labels.jpg... \n",
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"\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",
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"Image sizes 640 train, 640 val\n",
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"Using 8 dataloader workers\n",
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"Logging results to \u001b[1m/home/cuuva/experiment/swoon_detect/sw_detect/test\u001b[0m\n",
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"Starting training for 5 epochs...\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 1/5 18.7G 0.9334 3.789 0.9624 227 640: 100% ━━━━━━━━━━━━ 10/10 0.5it/s 21.8s.6ss\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.4it/s 1.3s1.1s\n",
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" all 588 553 0.00273 0.872 0.592 0.342\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 2/5 18.7G 0.9894 1.828 0.9224 217 640: 100% ━━━━━━━━━━━━ 10/10 1.1it/s 9.3s.5ss\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.2it/s 1.4s1.2s\n",
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" all 588 553 0.000806 0.0452 0.0232 0.019\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 3/5 18.7G 0.8474 0.8674 0.9355 223 640: 100% ━━━━━━━━━━━━ 10/10 0.3it/s 38.8s.5s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.5it/s 0.7s0.6s\n",
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" all 588 553 0 0 0 0\n",
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"\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 2 epochs. Best results observed at epoch 1, best model saved as best.pt.\n",
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"To update EarlyStopping(patience=2) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
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"\n",
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"3 epochs completed in 0.021 hours.\n",
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"Optimizer stripped from /home/cuuva/experiment/swoon_detect/sw_detect/test/weights/last.pt, 6.2MB\n",
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"Optimizer stripped from /home/cuuva/experiment/swoon_detect/sw_detect/test/weights/best.pt, 6.2MB\n",
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"\n",
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"Validating /home/cuuva/experiment/swoon_detect/sw_detect/test/weights/best.pt...\n",
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"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
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"Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 1.8it/s 1.7s1.5s\n",
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" all 588 553 0.00273 0.872 0.588 0.342\n",
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"Speed: 0.0ms preprocess, 0.2ms inference, 0.0ms loss, 1.0ms postprocess per image\n",
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"Results saved to \u001b[1m/home/cuuva/experiment/swoon_detect/sw_detect/test\u001b[0m\n"
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]
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}
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],
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"source": [
|
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"# Load a pretrained YOLO11n model\n",
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"model = YOLO(\"yolov8n.pt\")\n",
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"\n",
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"train_results = model.train(\n",
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" data=\"/home/cuuva/experiment/swoon_detect/swoon_detect.yaml\",\n",
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" epochs=5,\n",
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" imgsz=640,\n",
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" batch= -1,\n",
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" device=\"cuda\",\n",
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" optimizer = 'AdamW',\n",
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" lr0 = 0.001,\n",
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" patience = 2,\n",
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" project = 'sw_detect',\n",
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" name = 'test'\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "2c701957",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"New https://pypi.org/project/ultralytics/8.3.232 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",
|
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"\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/swoon_detect/swoon_detect.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=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=final_100epoch, 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=sw_detect, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch, 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",
|
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"Overriding model.yaml nc=80 with nc=1\n",
|
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"\n",
|
|
" from n params module arguments \n",
|
|
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
|
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" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
|
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" 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n",
|
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" 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
|
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" 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n",
|
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" 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
|
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" 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
|
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" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
|
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" 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n",
|
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" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
|
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" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
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" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
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" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
|
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" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
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" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
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" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
|
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" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
|
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" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
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" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
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" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
|
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" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
|
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" 22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] \n",
|
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"Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs\n",
|
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"\n",
|
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"Transferred 319/355 items from pretrained weights\n",
|
|
"Freezing layer 'model.22.dfl.conv.weight'\n",
|
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"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 9359.8±10220.7 MB/s, size: 3183.9 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01... 1294 images, 1884 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 3178/3178 11.4Kit/s 0.3s1s\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01.cache\n",
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"\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",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.33G total, 1.61G reserved, 0.19G allocated, 29.54G free\n",
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" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n",
|
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" 3011043 8.194 4.484 5.127 26.65 (1, 3, 640, 640) list\n",
|
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" 3011043 16.39 4.509 4.424 20.21 (2, 3, 640, 640) list\n",
|
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" 3011043 32.78 5.077 4.003 24.42 (4, 3, 640, 640) list\n",
|
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" 3011043 65.55 5.872 5.059 30.88 (8, 3, 640, 640) list\n",
|
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" 3011043 131.1 7.015 7.777 40.27 (16, 3, 640, 640) list\n",
|
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" 3011043 262.2 5.637 14.69 54.28 (32, 3, 640, 640) list\n",
|
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" 3011043 524.4 11.044 30.79 104 (64, 3, 640, 640) list\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 130 for CUDA:0 19.73G/31.33G (63%) ✅\n",
|
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 3966.5±3496.5 MB/s, size: 3344.7 KB)\n",
|
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Training/labels/01.cache... 1294 images, 1884 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 3178/3178 12.8Mit/s 0.0ss\n",
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"\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",
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"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 7169.7±6785.2 MB/s, size: 2736.1 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/swoon_detection/Validation/labels/03... 553 images, 838 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1391/1391 7.0Kit/s 0.2s.4s\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/swoon_detection/Validation/labels/03.cache\n",
|
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"Plotting labels to /home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/labels.jpg... \n",
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"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.001015625), 63 bias(decay=0.0)\n",
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"Image sizes 640 train, 640 val\n",
|
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"Using 8 dataloader workers\n",
|
|
"Logging results to \u001b[1m/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch\u001b[0m\n",
|
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"Starting training for 300 epochs...\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 1/300 19.9G 0.9969 3.111 0.9546 34 640: 100% ━━━━━━━━━━━━ 25/25 0.8it/s 32.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s0.2s\n",
|
|
" all 1391 553 0 0 0 0\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 2/300 19.9G 0.8853 0.81 0.9597 34 640: 100% ━━━━━━━━━━━━ 25/25 1.2it/s 21.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 8.7it/s 0.7s0.2s\n",
|
|
" all 1391 553 0 0 0 0\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 3/300 19.9G 0.838 0.714 0.9418 41 640: 100% ━━━━━━━━━━━━ 25/25 0.8it/s 32.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.614 0.467 0.552 0.417\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 4/300 19.9G 0.7963 0.6603 0.9216 45 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.9it/s 2.1s0.4s\n",
|
|
" all 1391 553 0.81 0.494 0.67 0.388\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 5/300 19.9G 0.8114 0.6338 0.9404 42 640: 100% ━━━━━━━━━━━━ 25/25 1.2it/s 21.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s0.4s\n",
|
|
" all 1391 553 0.902 0.764 0.818 0.543\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 6/300 19.9G 0.7744 0.5917 0.9234 37 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.0it/s 2.0s0.4s\n",
|
|
" all 1391 553 0.797 0.73 0.705 0.454\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 7/300 19.9G 0.7717 0.578 0.9199 34 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.8it/s 2.2s0.5s\n",
|
|
" all 1391 553 0.868 0.832 0.868 0.635\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 8/300 19.9G 0.7489 0.555 0.9154 44 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 1.9it/s 3.2s0.7ss\n",
|
|
" all 1391 553 0.819 0.765 0.803 0.547\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 9/300 19.9G 0.7256 0.5317 0.9021 38 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.0it/s 2.0s0.4s\n",
|
|
" all 1391 553 0.879 0.848 0.88 0.664\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 10/300 19.9G 0.7306 0.5187 0.912 46 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.8it/s 2.2s0.5s\n",
|
|
" all 1391 553 0.846 0.821 0.843 0.603\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 11/300 19.9G 0.7122 0.5281 0.9049 53 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.897 0.857 0.891 0.663\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 12/300 19.9G 0.6864 0.4963 0.8971 39 640: 100% ━━━━━━━━━━━━ 25/25 2.8it/s 9.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.3it/s 1.1s0.3s\n",
|
|
" all 1391 553 0.898 0.834 0.852 0.622\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 13/300 19.9G 0.6638 0.4757 0.8969 35 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.876 0.843 0.826 0.581\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 14/300 19.9G 0.6892 0.4911 0.8962 46 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.877 0.865 0.866 0.636\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 15/300 19.9G 0.6763 0.4774 0.8976 50 640: 100% ━━━━━━━━━━━━ 25/25 2.8it/s 9.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.9 0.832 0.869 0.639\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 16/300 19.9G 0.6348 0.4648 0.8834 50 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s0.3s\n",
|
|
" all 1391 553 0.889 0.872 0.881 0.667\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 17/300 19.9G 0.6552 0.4611 0.8838 37 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s0.4s\n",
|
|
" all 1391 553 0.892 0.834 0.86 0.622\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 18/300 19.9G 0.64 0.4523 0.8795 47 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.8it/s 2.1s0.4s\n",
|
|
" all 1391 553 0.884 0.816 0.852 0.634\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 19/300 19.9G 0.6227 0.4525 0.8849 40 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.5it/s 1.7s0.3s\n",
|
|
" all 1391 553 0.912 0.843 0.88 0.648\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 20/300 19.9G 0.6345 0.4443 0.8851 46 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.6it/s 2.3s0.5s\n",
|
|
" all 1391 553 0.877 0.823 0.817 0.555\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 21/300 19.9G 0.6382 0.443 0.8804 38 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.8it/s 2.1s0.5s\n",
|
|
" all 1391 553 0.895 0.852 0.861 0.605\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 22/300 19.9G 0.6077 0.4324 0.8729 59 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.3s0.4s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.5it/s 2.4s0.5s\n",
|
|
" all 1391 553 0.891 0.857 0.841 0.62\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 23/300 19.9G 0.6207 0.4323 0.8735 46 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.3it/s 2.6s0.6s\n",
|
|
" all 1391 553 0.91 0.832 0.871 0.663\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 24/300 19.9G 0.6181 0.4311 0.8788 55 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.5it/s 2.4s0.5s\n",
|
|
" all 1391 553 0.887 0.842 0.874 0.642\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 25/300 19.9G 0.6198 0.43 0.8777 50 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.6it/s 2.3s0.4ss\n",
|
|
" all 1391 553 0.902 0.832 0.873 0.688\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 26/300 19.9G 0.6166 0.423 0.8727 57 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.6it/s 2.3s0.5s\n",
|
|
" all 1391 553 0.936 0.868 0.901 0.679\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 27/300 19.9G 0.5914 0.4062 0.8667 43 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.7s0.2s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 6.3it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.921 0.846 0.888 0.658\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 28/300 19.9G 0.6104 0.4184 0.8687 37 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 6.1it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.908 0.863 0.878 0.655\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 29/300 19.9G 0.6058 0.418 0.8766 42 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.3s0.2s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.9it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.914 0.862 0.882 0.66\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 30/300 19.9G 0.606 0.4159 0.8768 48 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.4it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.894 0.853 0.897 0.7\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 31/300 19.9G 0.5879 0.4105 0.8654 41 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.907 0.866 0.885 0.667\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 32/300 19.9G 0.5835 0.401 0.8628 50 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.92 0.889 0.901 0.671\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 33/300 19.9G 0.5884 0.4116 0.8679 40 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.3it/s 1.1s0.3s\n",
|
|
" all 1391 553 0.921 0.848 0.911 0.695\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 34/300 19.9G 0.5913 0.4086 0.8747 41 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.917 0.856 0.886 0.648\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 35/300 19.9G 0.5735 0.4081 0.865 45 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.908 0.842 0.884 0.659\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 36/300 19.9G 0.5741 0.398 0.8661 40 640: 100% ━━━━━━━━━━━━ 25/25 1.8it/s 14.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.5it/s 1.7s0.4s\n",
|
|
" all 1391 553 0.909 0.866 0.899 0.67\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 37/300 19.9G 0.5699 0.4 0.8673 49 640: 100% ━━━━━━━━━━━━ 25/25 1.8it/s 13.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.906 0.87 0.874 0.621\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 38/300 19.9G 0.5422 0.3833 0.8599 46 640: 100% ━━━━━━━━━━━━ 25/25 1.7it/s 14.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.92 0.879 0.903 0.686\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 39/300 19.9G 0.5696 0.4002 0.8648 50 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.924 0.878 0.91 0.701\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 40/300 19.9G 0.5658 0.3939 0.8605 36 640: 100% ━━━━━━━━━━━━ 25/25 1.9it/s 13.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.8it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.905 0.875 0.907 0.713\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 41/300 19.9G 0.564 0.3844 0.8617 44 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.915 0.886 0.908 0.701\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 42/300 19.9G 0.5608 0.3903 0.8589 54 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.907 0.886 0.911 0.734\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 43/300 19.9G 0.5449 0.3804 0.8539 46 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.902 0.866 0.891 0.683\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 44/300 19.9G 0.5504 0.3723 0.856 57 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.7it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.938 0.879 0.912 0.721\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 45/300 19.9G 0.551 0.3745 0.8563 56 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s0.4s\n",
|
|
" all 1391 553 0.908 0.852 0.878 0.688\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 46/300 19.9G 0.5616 0.385 0.8683 45 640: 100% ━━━━━━━━━━━━ 25/25 1.9it/s 13.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.9it/s 2.1s0.5s\n",
|
|
" all 1391 553 0.908 0.859 0.874 0.644\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 47/300 19.9G 0.5547 0.3773 0.8551 47 640: 100% ━━━━━━━━━━━━ 25/25 2.0it/s 12.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.4it/s 1.8s0.4s\n",
|
|
" all 1391 553 0.913 0.877 0.901 0.69\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 48/300 19.9G 0.5665 0.3891 0.8615 50 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 2.8it/s 2.1s0.5s\n",
|
|
" all 1391 553 0.902 0.884 0.9 0.666\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 49/300 19.9G 0.5394 0.367 0.8585 38 640: 100% ━━━━━━━━━━━━ 25/25 2.1it/s 12.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s0.4s\n",
|
|
" all 1391 553 0.896 0.846 0.89 0.673\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 50/300 19.9G 0.5483 0.3649 0.8562 50 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.2it/s 1.9s0.4s\n",
|
|
" all 1391 553 0.898 0.881 0.903 0.714\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 51/300 19.9G 0.5461 0.364 0.8593 47 640: 100% ━━━━━━━━━━━━ 25/25 2.0it/s 12.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.0it/s 2.0s0.4s\n",
|
|
" all 1391 553 0.901 0.877 0.888 0.677\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 52/300 19.9G 0.5446 0.3702 0.8601 39 640: 100% ━━━━━━━━━━━━ 25/25 1.5it/s 16.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.907 0.883 0.896 0.684\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 53/300 19.9G 0.5291 0.3752 0.8544 50 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 6.1it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.895 0.882 0.901 0.691\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 54/300 19.9G 0.5239 0.368 0.8535 53 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.8it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.904 0.877 0.901 0.682\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 55/300 19.9G 0.537 0.3701 0.8593 46 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.3it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.922 0.881 0.906 0.695\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 56/300 19.9G 0.533 0.3727 0.8545 43 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.7it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.93 0.879 0.918 0.708\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 57/300 19.9G 0.5228 0.3638 0.8448 39 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s0.2s\n",
|
|
" all 1391 553 0.939 0.862 0.904 0.694\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 58/300 19.9G 0.5359 0.3678 0.8521 44 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.936 0.852 0.886 0.685\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 59/300 19.9G 0.5117 0.3556 0.8444 49 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.915 0.88 0.897 0.664\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 60/300 19.9G 0.5293 0.3584 0.8547 48 640: 100% ━━━━━━━━━━━━ 25/25 3.0it/s 8.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.3it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.918 0.888 0.906 0.68\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 61/300 19.9G 0.5365 0.3633 0.8528 46 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.947 0.884 0.921 0.705\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 62/300 19.9G 0.5191 0.3547 0.8463 41 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.0it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.927 0.877 0.898 0.683\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 63/300 19.9G 0.5105 0.3523 0.8513 43 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.929 0.884 0.902 0.702\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 64/300 19.9G 0.5207 0.3634 0.8527 37 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.92 0.87 0.882 0.68\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 65/300 19.9G 0.5079 0.3481 0.8507 47 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.907 0.881 0.9 0.699\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 66/300 19.9G 0.5114 0.3475 0.8548 33 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.905 0.854 0.898 0.691\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 67/300 19.9G 0.5163 0.3454 0.8524 51 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.9it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.923 0.884 0.908 0.715\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 68/300 19.9G 0.5081 0.3569 0.8485 39 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.942 0.863 0.905 0.667\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 69/300 19.9G 0.4998 0.3368 0.8435 54 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.926 0.885 0.903 0.683\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 70/300 19.9G 0.522 0.3523 0.8499 36 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.5it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.925 0.87 0.909 0.693\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 71/300 19.9G 0.5013 0.3389 0.8487 45 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.913 0.878 0.907 0.695\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 72/300 19.9G 0.4998 0.341 0.8455 47 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.934 0.893 0.921 0.722\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 73/300 19.9G 0.5137 0.3429 0.8435 55 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.933 0.875 0.898 0.697\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 74/300 19.9G 0.503 0.3392 0.8459 46 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.4it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.91 0.873 0.906 0.696\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 75/300 19.9G 0.4958 0.3447 0.844 42 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.923 0.864 0.896 0.689\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 76/300 19.9G 0.4903 0.3378 0.8434 51 640: 100% ━━━━━━━━━━━━ 25/25 2.9it/s 8.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.927 0.897 0.918 0.703\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 77/300 19.9G 0.494 0.326 0.8462 47 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.4it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.923 0.895 0.906 0.694\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 78/300 19.9G 0.4974 0.3364 0.8464 48 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.929 0.877 0.914 0.699\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 79/300 19.9G 0.4982 0.3408 0.8432 56 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.8s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.898 0.89 0.912 0.695\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 80/300 19.9G 0.5109 0.3441 0.8501 37 640: 100% ━━━━━━━━━━━━ 25/25 1.9it/s 13.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.877 0.877 0.878 0.654\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 81/300 19.9G 0.5039 0.3406 0.8496 46 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.915 0.872 0.915 0.743\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 82/300 19.9G 0.51 0.3365 0.8433 58 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.924 0.881 0.913 0.729\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 83/300 19.9G 0.4873 0.3315 0.8467 45 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 6.1it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.909 0.881 0.894 0.696\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 84/300 19.9G 0.5065 0.3362 0.845 42 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.905 0.908 0.911 0.7\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 85/300 19.9G 0.487 0.3276 0.8368 50 640: 100% ━━━━━━━━━━━━ 25/25 2.1it/s 12.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.0it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.927 0.884 0.91 0.715\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 86/300 19.9G 0.4871 0.3255 0.8377 47 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.905 0.857 0.898 0.687\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 87/300 19.9G 0.4899 0.3338 0.8445 52 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.907 0.859 0.893 0.705\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 88/300 19.9G 0.4777 0.3208 0.8411 41 640: 100% ━━━━━━━━━━━━ 25/25 2.1it/s 12.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.913 0.877 0.905 0.719\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 89/300 19.9G 0.4629 0.3135 0.8375 42 640: 100% ━━━━━━━━━━━━ 25/25 2.1it/s 12.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.92 0.875 0.905 0.7\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 90/300 19.9G 0.4747 0.3203 0.8383 35 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.941 0.852 0.913 0.699\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 91/300 19.9G 0.4727 0.3183 0.8375 55 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.954 0.868 0.914 0.73\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 92/300 19.9G 0.4756 0.3235 0.8392 47 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.937 0.854 0.907 0.712\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 93/300 19.9G 0.4693 0.3194 0.8356 40 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.909 0.869 0.888 0.681\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 94/300 19.9G 0.4753 0.3269 0.8409 50 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.942 0.855 0.901 0.692\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 95/300 19.9G 0.4841 0.329 0.8422 43 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.4s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.928 0.839 0.897 0.71\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 96/300 19.9G 0.4791 0.3228 0.8447 39 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.9it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.903 0.86 0.914 0.718\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 97/300 19.9G 0.4851 0.3274 0.8438 38 640: 100% ━━━━━━━━━━━━ 25/25 1.9it/s 13.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s0.4s\n",
|
|
" all 1391 553 0.925 0.891 0.913 0.701\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 98/300 19.9G 0.4764 0.3179 0.8428 32 640: 100% ━━━━━━━━━━━━ 25/25 1.9it/s 13.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s0.4s\n",
|
|
" all 1391 553 0.957 0.886 0.925 0.708\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 99/300 19.9G 0.4807 0.3315 0.8399 47 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s0.3s\n",
|
|
" all 1391 553 0.931 0.873 0.912 0.697\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 100/300 19.9G 0.4728 0.3198 0.8406 39 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s0.4s\n",
|
|
" all 1391 553 0.902 0.868 0.893 0.693\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 101/300 19.9G 0.4708 0.3148 0.8384 32 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.914 0.854 0.903 0.712\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 102/300 19.9G 0.4622 0.3204 0.8428 64 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.925 0.847 0.897 0.691\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 103/300 19.9G 0.4758 0.3242 0.8412 42 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.911 0.87 0.883 0.669\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 104/300 19.9G 0.4461 0.3066 0.8325 45 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.5it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.915 0.863 0.888 0.699\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 105/300 19.9G 0.4686 0.3156 0.8369 55 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.921 0.861 0.898 0.712\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 106/300 19.9G 0.4592 0.3085 0.8365 44 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.0it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.916 0.861 0.901 0.703\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 107/300 19.9G 0.4587 0.3063 0.838 44 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.6s0.4s\n",
|
|
" all 1391 553 0.91 0.892 0.918 0.716\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 108/300 19.9G 0.4754 0.3197 0.8439 62 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.4s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.923 0.877 0.899 0.68\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 109/300 19.9G 0.4655 0.3134 0.8359 48 640: 100% ━━━━━━━━━━━━ 25/25 2.0it/s 12.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.928 0.879 0.903 0.714\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 110/300 19.9G 0.4585 0.3139 0.8412 32 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.933 0.875 0.916 0.7\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 111/300 19.9G 0.4459 0.304 0.8292 47 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.3s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.945 0.884 0.907 0.702\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 112/300 19.9G 0.4553 0.3107 0.8385 40 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 9.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.0it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.905 0.872 0.9 0.692\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 113/300 19.9G 0.4645 0.311 0.836 33 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.1s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.9it/s 1.0s0.2s\n",
|
|
" all 1391 553 0.911 0.879 0.906 0.705\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 114/300 19.9G 0.47 0.3158 0.8351 47 640: 100% ━━━━━━━━━━━━ 25/25 2.5it/s 10.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.908 0.873 0.894 0.687\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 115/300 19.9G 0.4566 0.31 0.8379 43 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.911 0.87 0.882 0.69\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 116/300 19.9G 0.4539 0.3119 0.8353 51 640: 100% ━━━━━━━━━━━━ 25/25 2.8it/s 9.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.917 0.873 0.894 0.722\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 117/300 19.9G 0.4511 0.306 0.8316 47 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.2it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.934 0.889 0.912 0.7\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 118/300 19.9G 0.4345 0.2954 0.8263 44 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.923 0.872 0.896 0.694\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 119/300 19.9G 0.448 0.3034 0.8335 43 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.916 0.865 0.894 0.71\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 120/300 19.9G 0.4626 0.3175 0.84 52 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.0it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.941 0.863 0.911 0.712\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 121/300 19.9G 0.453 0.3118 0.8399 56 640: 100% ━━━━━━━━━━━━ 25/25 2.1it/s 11.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.2it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.934 0.889 0.909 0.689\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 122/300 19.9G 0.4568 0.309 0.8397 34 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.9it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.921 0.888 0.907 0.731\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 123/300 19.9G 0.4371 0.2953 0.829 55 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.931 0.872 0.896 0.706\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 124/300 19.9G 0.447 0.3018 0.8286 43 640: 100% ━━━━━━━━━━━━ 25/25 2.8it/s 8.9s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s0.3s\n",
|
|
" all 1391 553 0.899 0.881 0.902 0.693\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 125/300 19.9G 0.4288 0.2904 0.8251 39 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 10.7s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.2s0.3s\n",
|
|
" all 1391 553 0.923 0.872 0.892 0.686\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 126/300 19.9G 0.4351 0.2947 0.8236 58 640: 100% ━━━━━━━━━━━━ 25/25 2.7it/s 9.4s0.3s\n",
|
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 5.4it/s 1.1s0.2s\n",
|
|
" all 1391 553 0.92 0.877 0.901 0.68\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 127/300 19.9G 0.4362 0.2969 0.833 46 640: 100% ━━━━━━━━━━━━ 25/25 2.3it/s 11.0s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.939 0.877 0.911 0.72\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 128/300 19.9G 0.4424 0.2982 0.8347 40 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s0.3s\n",
|
|
" all 1391 553 0.912 0.893 0.908 0.705\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 129/300 19.9G 0.4445 0.3079 0.8389 41 640: 100% ━━━━━━━━━━━━ 25/25 2.6it/s 9.6s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s0.3s\n",
|
|
" all 1391 553 0.925 0.89 0.904 0.701\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 130/300 19.9G 0.44 0.3038 0.831 51 640: 100% ━━━━━━━━━━━━ 25/25 2.2it/s 11.5s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s0.3s\n",
|
|
" all 1391 553 0.921 0.892 0.908 0.709\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
"\u001b[K 131/300 19.9G 0.4385 0.2991 0.8269 58 640: 100% ━━━━━━━━━━━━ 25/25 2.4it/s 10.2s0.3s\n",
|
|
"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s0.4s\n",
|
|
" all 1391 553 0.927 0.877 0.898 0.695\n",
|
|
"\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 50 epochs. Best results observed at epoch 81, best model saved as best.pt.\n",
|
|
"To update EarlyStopping(patience=50) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
|
|
"\n",
|
|
"131 epochs completed in 0.475 hours.\n",
|
|
"Optimizer stripped from /home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/last.pt, 6.2MB\n",
|
|
"Optimizer stripped from /home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.pt, 6.2MB\n",
|
|
"\n",
|
|
"Validating /home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/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% ━━━━━━━━━━━━ 6/6 2.1it/s 2.9s0.7s\n",
|
|
" all 1391 553 0.915 0.872 0.915 0.743\n",
|
|
"Speed: 0.1ms preprocess, 0.2ms inference, 0.0ms loss, 0.6ms postprocess per image\n",
|
|
"Results saved to \u001b[1m/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch\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/swoon_detect/swoon_detect.yaml\",\n",
|
|
" epochs=300,\n",
|
|
" imgsz=640,\n",
|
|
" batch= -1,\n",
|
|
" device=\"cuda\",\n",
|
|
" optimizer = 'AdamW',\n",
|
|
" lr0 = 0.001,\n",
|
|
" patience = 50,\n",
|
|
" project = 'sw_detect',\n",
|
|
" name = 'final_100epoch'\n",
|
|
")"
|
|
]
|
|
},
|
|
{
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"cell_type": "code",
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"execution_count": 14,
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"id": "e8e8027a",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32087MiB)\n",
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"Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs\n",
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"\n",
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"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 5, 8400) (5.9 MB)\n",
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"\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 0.5s, saved as '/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.onnx' (11.7 MB)\n",
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"\n",
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"Export complete (0.7s)\n",
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"Results saved to \u001b[1m/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights\u001b[0m\n",
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"Predict: yolo predict task=detect model=/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.onnx imgsz=640 \n",
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"Validate: yolo val task=detect model=/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.onnx imgsz=640 data=/home/cuuva/experiment/swoon_detect/swoon_detect.yaml \n",
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"Visualize: https://netron.app\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.onnx'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model = YOLO(\"/home/cuuva/experiment/swoon_detect/sw_detect/final_100epoch/weights/best.pt\")\n",
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"model.export(format=\"onnx\", imgsz=640, device=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "0b7ad426",
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"metadata": {},
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"outputs": [
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{
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"✔ 변환 완료 → swoon_09_t000075.jpg\n",
|
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"✔ 변환 완료 → swoon_21_t000050.jpg\n",
|
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"✔ 변환 완료 → 10_frame_000030.jpg\n",
|
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"✔ 변환 완료 → swoon_01_t000043.jpg\n",
|
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"✔ 변환 완료 → swoon_07_t000058.jpg\n",
|
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"✔ 변환 완료 → swoon_16_t000088.jpg\n",
|
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"✔ 변환 완료 → swoon_06_t000009.jpg\n",
|
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"✔ 변환 완료 → swoon_06_t000044.jpg\n",
|
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"✔ 변환 완료 → swoon_02_t000024.jpg\n",
|
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"✔ 변환 완료 → 21_frame_000079.jpg\n",
|
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"✔ 변환 완료 → 06_frame_000061.jpg\n",
|
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"✔ 변환 완료 → swoon_02_t000076.jpg\n",
|
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"✔ 변환 완료 → 19_frame_000051.jpg\n",
|
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"✔ 변환 완료 → swoon_10_t000012.jpg\n",
|
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"✔ 변환 완료 → swoon_20_t000021.jpg\n",
|
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"✔ 변환 완료 → swoon_09_t000026.jpg\n",
|
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"✔ 변환 완료 → 07_frame_000067.jpg\n",
|
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"✔ 변환 완료 → swoon_16_t000027.jpg\n",
|
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"✔ 변환 완료 → swoon_21_t000062.jpg\n",
|
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"✔ 변환 완료 → 10_frame_000046.jpg\n",
|
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"✔ 변환 완료 → swoon_10_t000022.jpg\n",
|
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"✔ 변환 완료 → swoon_09_t000009.jpg\n",
|
|
"✔ 변환 완료 → swoon_07_t000035.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000058.jpg\n",
|
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"✔ 변환 완료 → swoon_04_t000070.jpg\n",
|
|
"✔ 변환 완료 → swoon_07_t000011.jpg\n",
|
|
"✔ 변환 완료 → 07_frame_000000.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000030.jpg\n",
|
|
"✔ 변환 완료 → 24_frame_000045.jpg\n",
|
|
"✔ 변환 완료 → 05_frame_000120.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000043.jpg\n",
|
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"✔ 변환 완료 → swoon_05_t000006.jpg\n",
|
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"✔ 변환 완료 → swoon_07_t000031.jpg\n",
|
|
"✔ 변환 완료 → 24_frame_000072.jpg\n",
|
|
"✔ 변환 완료 → swoon_16_t000046.jpg\n",
|
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"✔ 변환 완료 → swoon_02_t000015.jpg\n",
|
|
"✔ 변환 완료 → swoon_10_t000067.jpg\n",
|
|
"✔ 변환 완료 → swoon_12_t000011.jpg\n",
|
|
"✔ 변환 완료 → swoon_21_t000059.jpg\n",
|
|
"✔ 변환 완료 → 10_frame_000060.jpg\n",
|
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"✔ 변환 완료 → swoon_07_t000023.jpg\n",
|
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"✔ 변환 완료 → swoon_20_t000045.jpg\n",
|
|
"✔ 변환 완료 → swoon_05_t000063.jpg\n",
|
|
"✔ 변환 완료 → swoon_10_t000045.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000014.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000022.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000033.jpg\n",
|
|
"✔ 변환 완료 → 10_frame_000009.jpg\n",
|
|
"✔ 변환 완료 → swoon_24_t000082.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000079.jpg\n",
|
|
"✔ 변환 완료 → 06_frame_000029.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000068.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000060.jpg\n",
|
|
"✔ 변환 완료 → 02_frame_000052.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000042.jpg\n",
|
|
"✔ 변환 완료 → 17_frame_000025.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000047.jpg\n",
|
|
"✔ 변환 완료 → 12_frame_000017.jpg\n",
|
|
"✔ 변환 완료 → swoon_02_t000071.jpg\n",
|
|
"✔ 변환 완료 → swoon_22_t000042.jpg\n",
|
|
"✔ 변환 완료 → swoon_06_t000088.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000009.jpg\n",
|
|
"✔ 변환 완료 → swoon_21_t000037.jpg\n",
|
|
"✔ 변환 완료 → swoon_10_t000033.jpg\n",
|
|
"✔ 변환 완료 → 09_frame_000045.jpg\n",
|
|
"✔ 변환 완료 → swoon_07_t000014.jpg\n",
|
|
"✔ 변환 완료 → 17_frame_000002.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000056.jpg\n",
|
|
"✔ 변환 완료 → 16_frame_000002.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000041.jpg\n",
|
|
"✔ 변환 완료 → swoon_22_t000089.jpg\n",
|
|
"✔ 변환 완료 → 16_frame_000025.jpg\n",
|
|
"✔ 변환 완료 → swoon_02_t000052.jpg\n",
|
|
"✔ 변환 완료 → swoon_05_t000051.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000039.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000019.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000065.jpg\n",
|
|
"✔ 변환 완료 → swoon_21_t000023.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000025.jpg\n",
|
|
"✔ 변환 완료 → swoon_04_t000056.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000018.jpg\n",
|
|
"✔ 변환 완료 → 22_frame_000053.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000068.jpg\n",
|
|
"✔ 변환 완료 → swoon_01_t000074.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000029.jpg\n",
|
|
"✔ 변환 완료 → swoon_02_t000048.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000062.jpg\n",
|
|
"✔ 변환 완료 → 02_frame_000124.jpg\n",
|
|
"✔ 변환 완료 → swoon_24_t000037.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000038.jpg\n",
|
|
"✔ 변환 완료 → swoon_10_t000081.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000035.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000070.jpg\n",
|
|
"✔ 변환 완료 → 07_frame_000012.jpg\n",
|
|
"✔ 변환 완료 → 06_frame_000116.jpg\n",
|
|
"✔ 변환 완료 → 05_frame_000109.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000075.jpg\n",
|
|
"✔ 변환 완료 → swoon_12_t000071.jpg\n",
|
|
"✔ 변환 완료 → swoon_19_t000087.jpg\n",
|
|
"✔ 변환 완료 → swoon_05_t000032.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000082.jpg\n",
|
|
"✔ 변환 완료 → 01_frame_000144.jpg\n",
|
|
"✔ 변환 완료 → swoon_17_t000079.jpg\n",
|
|
"✔ 변환 완료 → swoon_05_t000081.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000024.jpg\n",
|
|
"✔ 변환 완료 → swoon_05_t000086.jpg\n",
|
|
"✔ 변환 완료 → swoon_16_t000061.jpg\n",
|
|
"✔ 변환 완료 → 07_frame_000037.jpg\n",
|
|
"✔ 변환 완료 → swoon_20_t000050.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000046.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000058.jpg\n",
|
|
"✔ 변환 완료 → swoon_22_t000024.jpg\n",
|
|
"✔ 변환 완료 → swoon_09_t000038.jpg\n",
|
|
"✔ 변환 완료 → swoon_07_t000064.jpg\n",
|
|
"\n",
|
|
"🔥 완료! 총 300개 이미지가 640x384 해상도로 변환되었습니다.\n",
|
|
"📂 저장 위치: /home/cuuva/다운로드/Quant_swoon/Quant_swoon_resize\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import os\n",
|
|
"import cv2\n",
|
|
"\n",
|
|
"src_dir = \"/home/cuuva/다운로드/Quant_swoon/external1\"\n",
|
|
"dst_dir = \"/home/cuuva/다운로드/Quant_swoon/Quant_swoon_resize\"\n",
|
|
"\n",
|
|
"# 만들 폴더가 없으면 생성\n",
|
|
"os.makedirs(dst_dir, exist_ok=True)\n",
|
|
"\n",
|
|
"# 원하는 크기\n",
|
|
"target_size = (640, 384) # (width, height)\n",
|
|
"\n",
|
|
"count = 0\n",
|
|
"\n",
|
|
"for file in os.listdir(src_dir):\n",
|
|
" if file.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
|
|
" img_path = os.path.join(src_dir, file)\n",
|
|
" save_path = os.path.join(dst_dir, file)\n",
|
|
"\n",
|
|
" img = cv2.imread(img_path)\n",
|
|
"\n",
|
|
" if img is None:\n",
|
|
" print(f\"⚠ 이미지 읽기 실패 → {file}\")\n",
|
|
" continue\n",
|
|
"\n",
|
|
" resized = cv2.resize(img, target_size, interpolation=cv2.INTER_AREA)\n",
|
|
" cv2.imwrite(save_path, resized)\n",
|
|
"\n",
|
|
" count += 1\n",
|
|
" print(f\"✔ 변환 완료 → {file}\")\n",
|
|
"\n",
|
|
"print(f\"\\n🔥 완료! 총 {count}개 이미지가 640x384 해상도로 변환되었습니다.\")\n",
|
|
"print(f\"📂 저장 위치: {dst_dir}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d3de7684",
|
|
"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
|
|
}
|