{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":12475735,"sourceType":"datasetVersion","datasetId":7871309}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install ultralytics opencv-python","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%%writefile data.yaml\n\ntrain: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images\nval: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/valid/images\ntest: /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/test/images\n\nnc: 17\nnames:\n - 'Barefoots'\n - 'Ear-protection'\n - 'Harness'\n - 'No_Ear-Protection'\n - 'No_Glasses'\n - 'Sandals'\n - 'boots'\n - 'face_mask'\n - 'face_nomask'\n - 'glasses'\n - 'hand_glove'\n - 'hand_noglove'\n - 'head_helmet'\n - 'head_nohelmet'\n - 'person'\n - 'shoes'\n - 'vest'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-15T10:37:37.034389Z","iopub.execute_input":"2025-07-15T10:37:37.034953Z","iopub.status.idle":"2025-07-15T10:37:37.040386Z","shell.execute_reply.started":"2025-07-15T10:37:37.034925Z","shell.execute_reply":"2025-07-15T10:37:37.039539Z"}},"outputs":[{"name":"stdout","text":"Overwriting data.yaml\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"!yolo detect train data=data.yaml model=yolov8n.pt epochs=10 imgsz=640 batch=16 device=0,1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-15T11:18:00.156297Z","iopub.execute_input":"2025-07-15T11:18:00.156984Z","iopub.status.idle":"2025-07-15T12:03:21.801048Z","shell.execute_reply.started":"2025-07-15T11:18:00.156954Z","shell.execute_reply":"2025-07-15T12:03:21.800025Z"}},"outputs":[{"name":"stdout","text":"New https://pypi.org/project/ultralytics/8.3.167 available šŸ˜ƒ Update with 'pip install -U ultralytics'\nUltralytics 8.3.166 šŸš€ Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n CUDA:1 (Tesla T4, 15095MiB)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=10, 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=0.01, 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=train4, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train4, 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\nOverriding model.yaml nc=80 with nc=17\n\n from n params module arguments \n 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n 22 [15, 18, 21] 1 754627 ultralytics.nn.modules.head.Detect [17, [64, 128, 256]] \nModel summary: 129 layers, 3,014,163 parameters, 3,014,147 gradients, 8.2 GFLOPs\n\nTransferred 319/355 items from pretrained weights\n\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 56587 /root/.config/Ultralytics/DDP/_temp_sasvsj33131961272156368.py\nUltralytics 8.3.166 šŸš€ Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n CUDA:1 (Tesla T4, 15095MiB)\nOverriding model.yaml nc=80 with nc=17\nTransferred 319/355 items from pretrained weights\nFreezing layer 'model.22.dfl.conv.weight'\n\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed āœ…\n\u001b[34m\u001b[1mtrain: \u001b[0mFast image access āœ… (ping: 0.0Ā±0.0 ms, read: 38.5Ā±17.2 MB/s, size: 54.1 KB)\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-1-mp40_jpg.rf.d01f57d39ddf7b0da68e999207153d2f.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-1-mp42_jpg.rf.283e69143e7c30f0faae2a0dff17362c.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-112_jpg.rf.59003700ede444f10f0295de3f9c564c.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-112_jpg.rf.7b39ff350b7ad1eccef20195f19337c7.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-11_jpg.rf.796fa0b13b7e648bc11b6aedad08fb96.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-1-1-1-11_jpg.rf.c9f7458ffd4a4770d8c74bd3b677059b.jpg: 6 duplicate labels removed\n\u001b[34m\u001b[1mtrain: 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5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-2-1-4-28_jpg.rf.86d3bbec936f3d4aab6556c6fe55939a.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-3-1-1-26_jpg.rf.9ef6d8d04656bd5ad2753c7838dbfe2a.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-3-1-2-35_jpg.rf.a80f1fa46b3b92146f7b1ac96c69d71b.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-01-15-1-3-1-5-29_jpg.rf.e7dc20dc5ab52fd8d0ca6851cb61ec07.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe 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detection.v3i.yolov8/train/images/-Casual-Shoes-Summer-Outdoor-Slip-on-Slippers-Men-Shoes-footwear-jpg_640x640_jpg.rf.15c65fdf1e39379b64ee7e3daecbaf30.jpg: 9 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-_696_png.rf.306c69e7c2cc97baa8c108df17ed509d.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/-_jpg.rf.e8e1e7244b78b4db0168a1609f630f44.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/001113_jpg.rf.3e01f3e5b9d307fd3c991967665de0a9.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/001113_jpg.rf.478088f44c731862df0f1c8d091daaad.jpg: 2 duplicate labels 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2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-508121319-612x612_jpg.rf.457fddf239930a0e8bc659469e4584d4.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-508121319-612x612_jpg.rf.64209e6a474da8327dfa6e9ef4847bba.jpg: 6 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-514366692-612x612_jpg.rf.6c32f15192c2abf718b3e32d8ec38c7e.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-514366692-612x612_jpg.rf.f9e5b06e998d262e47f22443f9cac80f.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.1072ef227298a8411cefdb6c070769f6.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.30add10c2cad696bd2f84faa05d9016a.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-516012290-612x612_jpg.rf.d49ebdaa954c0a7a76853418c0b5d055.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.0e3960ee2af61a1173506c59bc05fccb.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.759948f30c09ae9117cf112d4962886b.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-691703536-612x612_jpg.rf.db435f1dd5ee84b9790f08637b349bd9.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.6b27b5fa7e895ba681cfbdee5888e7ed.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.c39aed43a5e4c129c71cc2039ac2e32e.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-807680780-612x612_jpg.rf.cffdda45225d80748bac9acd828a1586.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.4cb1fe65109500b175ef9dda6ec5b149.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.ae29b471e1b73df861499d978154b84c.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-916731436-612x612_jpg.rf.e1b7bcaad9ffcdc683c574780e370e1c.jpg: 3 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.0118aba0f13fd807f403809332470d45.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.3f935a9327167c8b626cac8e4f843bc5.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/gettyimages-950956144-612x612_jpg.rf.ba8e76badfca8313f71b0e3433c3a614.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/hard_hat_workers1041_png.rf.1d7372cb698a25bcc0336480c681008a.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/image_209_jpg.rf.06b4002494b1dbf38541344c6573786e.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/imagem_processada_1_jpg.rf.5c33212824cdee5ac2d699f1872d4ca9.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images-14-_jpg.rf.5f953c6e5802a23ba82fe19887e6b586.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images-14-_jpg.rf.7fcbefa1605bc3bdd63b556d1375ecf7.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images172_jpg.rf.87a3c988463a031ebff7015439748996.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images172_jpg.rf.c98d1a5f256e6f0edbe933bac097a0c1.jpg: 7 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images254_jpg.rf.f0e8b95f92a5d93adf4e3c09eb9007c6.jpg: 5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/images27_jpg.rf.d75234fcb95b13b146be0875e3b0b342.jpg: 4 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-1040921246-612x612_jpg.rf.c325c156a0f79a8e3759fd38fc5ed2e5.jpg: 2 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-1301543637-612x612_jpg.rf.0602568f4aaa2771f703c4c1da883c8f.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/istockphoto-92003976-612x612_jpg.rf.20775ce01cd3e701b2373c07b68a9a99.jpg: 5 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/youtube-109_jpg.rf.d2906848f52cf8bf5dd36d1e5c7f2025.jpg: 1 duplicate labels removed\n\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train/images/youtube-52_jpg.rf.849c4f137696732bd810d2a4dcfe3c86.jpg: 2 duplicate labels removed\nWARNING āš ļø \u001b[34m\u001b[1mtrain: \u001b[0mCache directory /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/train is not writeable, cache not saved.\nWARNING āš ļø Box and segment counts should be equal, but got len(segments) = 41264, len(boxes) = 219348. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\u001b[34m\u001b[1mval: \u001b[0mFast image access āœ… (ping: 0.4Ā±0.8 ms, read: 42.6Ā±18.9 MB/s, size: 51.5 KB)\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.y\u001b[0m\nWARNING āš ļø \u001b[34m\u001b[1mval: \u001b[0mCache directory /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i.yolov8/valid is not writeable, cache not saved.\nWARNING āš ļø Box and segment counts should be equal, but got len(segments) = 333, len(boxes) = 8996. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0mPlotting labels to runs/detect/train4/labels.jpg... \n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000476, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\nImage sizes 640 train, 640 val\nUsing 4 dataloader workers\nLogging results to \u001b[1mruns/detect/train4\u001b[0m\nStarting training for 10 epochs...\nClosing dataloader mosaic\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[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/ppe-objection-detection/datasets/ppe detection.v3i\u001b[0m\n 1/10 1.35G 1.555 2.599 1.607 9 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.537 0.522 0.518 0.289\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 2/10 1.67G 1.442 1.88 1.512 13 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.62 0.574 0.584 0.335\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 3/10 1.69G 1.4 1.711 1.481 13 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.639 0.61 0.619 0.361\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 4/10 1.7G 1.362 1.61 1.458 20 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.673 0.623 0.646 0.381\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 5/10 1.72G 1.319 1.506 1.419 10 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.685 0.654 0.665 0.396\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 6/10 1.73G 1.284 1.43 1.391 24 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.666 0.683 0.678 0.412\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 7/10 1.75G 1.264 1.381 1.377 10 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.7 0.691 0.709 0.43\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 8/10 1.77G 1.238 1.329 1.354 14 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.713 0.69 0.718 0.439\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 9/10 1.79G 1.208 1.28 1.336 22 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.7 0.722 0.726 0.448\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n 10/10 1.8G 1.189 1.24 1.322 21 640: 1\n Class Images Instances Box(P R mAP50 m\n all 1636 8996 0.719 0.714 0.734 0.456\n\n10 epochs completed in 0.720 hours.\nOptimizer stripped from runs/detect/train4/weights/last.pt, 6.2MB\nOptimizer stripped from runs/detect/train4/weights/best.pt, 6.2MB\n\nValidating runs/detect/train4/weights/best.pt...\nUltralytics 8.3.166 šŸš€ Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n CUDA:1 (Tesla T4, 15095MiB)\nModel summary (fused): 72 layers, 3,008,963 parameters, 0 gradients, 8.1 GFLOPs\n Class Images Instances Box(P R mAP50 m\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n xa[xa < 0] = -1\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n xa[xa < 0] = -1\n all 1636 8996 0.72 0.715 0.735 0.456\n Barefoots 172 327 0.861 0.914 0.95 0.668\n Ear-protection 153 235 0.518 0.586 0.552 0.34\n Harness 237 347 0.719 0.738 0.772 0.476\n No_Ear-Protection 115 153 0.43 0.34 0.36 0.205\n No_Glasses 80 95 0.494 0.568 0.482 0.244\n Sandals 139 199 0.684 0.884 0.801 0.501\n boots 313 661 0.92 0.82 0.903 0.662\n face_mask 309 399 0.733 0.797 0.748 0.384\n face_nomask 359 563 0.753 0.726 0.783 0.39\n glasses 328 401 0.757 0.739 0.742 0.411\n hand_glove 449 830 0.774 0.657 0.672 0.392\n hand_noglove 305 620 0.682 0.373 0.496 0.228\n head_helmet 436 704 0.784 0.824 0.866 0.584\n head_nohelmet 432 846 0.832 0.817 0.874 0.535\n person 831 1488 0.782 0.88 0.895 0.697\n shoes 232 547 0.667 0.59 0.664 0.333\n vest 398 581 0.841 0.897 0.935 0.705\nSpeed: 0.2ms preprocess, 2.1ms inference, 0.0ms loss, 1.5ms postprocess per image\nResults saved to \u001b[1mruns/detect/train4\u001b[0m\nšŸ’” Learn more at https://docs.ultralytics.com/modes/train\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}