yolov8inssurgical.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":8745613,"sourceType":"datasetVersion","datasetId":5251538}],"dockerImageVersionId":30733,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%pip install -q \"openvino>=2024.0.0\" \"nncf>=2.9.0\"\n%pip install -q \"torch>=2.1\" \"torchvision>=0.16\" \"ultralytics==8.2.24\" onnx opencv-python tqdm --extra-index-url https://download.pytorch.org/whl/cpu","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-21T08:47:07.681912Z","iopub.execute_input":"2024-06-21T08:47:07.682192Z","iopub.status.idle":"2024-06-21T08:47:45.688434Z","shell.execute_reply.started":"2024-06-21T08:47:07.682165Z","shell.execute_reply":"2024-06-21T08:47:45.687220Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Note: you may need to restart the kernel to use updated packages.\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"%pip install -q \"openvino>=2024.0.0\" \"nncf>=2.9.0\"\n%pip install -q \"torch>=2.1\" \"torchvision>=0.16\" \"ultralytics==8.2.24\" onnx opencv-python tqdm --extra-index-url https://download.pytorch.org/whl/cpu","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:48:28.610702Z","iopub.execute_input":"2024-06-21T08:48:28.611079Z","iopub.status.idle":"2024-06-21T08:48:53.700027Z","shell.execute_reply.started":"2024-06-21T08:48:28.611045Z","shell.execute_reply":"2024-06-21T08:48:53.698760Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Note: you may need to restart the kernel to use updated packages.\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"from IPython import display\ndisplay.clear_output()\n\nimport ultralytics\nultralytics.checks()","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:48:53.702380Z","iopub.execute_input":"2024-06-21T08:48:53.702860Z","iopub.status.idle":"2024-06-21T08:49:08.351607Z","shell.execute_reply.started":"2024-06-21T08:48:53.702819Z","shell.execute_reply":"2024-06-21T08:49:08.350663Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Ultralytics YOLOv8.2.24 🚀 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla T4, 15102MiB)\nSetup complete ✅ (4 CPUs, 31.4 GB RAM, 5689.5/8062.4 GB disk)\n","output_type":"stream"}]},{"cell_type":"code","source":"from ultralytics import YOLO\nfrom IPython.display import display, Image\nimport os\nHOME = os.getcwd()\nprint(HOME)","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:49:08.352670Z","iopub.execute_input":"2024-06-21T08:49:08.353038Z","iopub.status.idle":"2024-06-21T08:49:08.358516Z","shell.execute_reply.started":"2024-06-21T08:49:08.353014Z","shell.execute_reply":"2024-06-21T08:49:08.357492Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"/kaggle/working\n","output_type":"stream"}]},{"cell_type":"code","source":"model = YOLO('yolov8s-seg.pt')\n","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:49:08.360600Z","iopub.execute_input":"2024-06-21T08:49:08.360940Z","iopub.status.idle":"2024-06-21T08:49:09.066542Z","shell.execute_reply.started":"2024-06-21T08:49:08.360909Z","shell.execute_reply":"2024-06-21T08:49:09.065776Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt to 'yolov8s-seg.pt'...\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 22.8M/22.8M [00:00<00:00, 163MB/s] \n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install roboflow\n\nfrom roboflow import Roboflow\nrf = Roboflow(api_key=\"vAMiGdZPSrKa7SjlzjXk\")\nproject = rf.workspace(\"mj-4iyes\").project(\"surgical_tool_segmentation_mj\")\nversion = project.version(6)\ndataset = version.download(\"yolov8\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:49:11.862300Z","iopub.execute_input":"2024-06-21T08:49:11.862655Z","iopub.status.idle":"2024-06-21T08:49:33.237584Z","shell.execute_reply.started":"2024-06-21T08:49:11.862628Z","shell.execute_reply":"2024-06-21T08:49:33.236620Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Collecting roboflow\n  Downloading roboflow-1.1.33-py3-none-any.whl.metadata (9.2 kB)\nRequirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from roboflow) (2024.2.2)\nCollecting chardet==4.0.0 (from roboflow)\n  Downloading chardet-4.0.0-py2.py3-none-any.whl.metadata (3.5 kB)\nCollecting idna==3.7 (from roboflow)\n  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\nRequirement already satisfied: cycler in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.12.1)\nRequirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.4.5)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from roboflow) (3.7.5)\nRequirement already satisfied: numpy>=1.18.5 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.26.4)\nCollecting opencv-python-headless==4.10.0.84 (from roboflow)\n  Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\nRequirement already satisfied: Pillow>=7.1.2 in /opt/conda/lib/python3.10/site-packages (from roboflow) (9.5.0)\nRequirement already satisfied: python-dateutil in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.9.0.post0)\nRequirement already satisfied: python-dotenv in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.0.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from roboflow) (2.32.3)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.16.0)\nRequirement already satisfied: urllib3>=1.26.6 in /opt/conda/lib/python3.10/site-packages (from roboflow) (1.26.18)\nRequirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.10/site-packages (from roboflow) (4.66.4)\nRequirement already satisfied: PyYAML>=5.3.1 in /opt/conda/lib/python3.10/site-packages (from roboflow) (6.0.1)\nRequirement already satisfied: requests-toolbelt in /opt/conda/lib/python3.10/site-packages (from roboflow) (0.10.1)\nCollecting python-magic (from roboflow)\n  Downloading python_magic-0.4.27-py2.py3-none-any.whl.metadata (5.8 kB)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (1.2.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (4.47.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (21.3)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->roboflow) (3.1.1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->roboflow) (3.3.2)\nDownloading roboflow-1.1.33-py3-none-any.whl (75 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hDownloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n\u001b[2K   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4.10.0.82\n    Uninstalling opencv-python-headless-4.10.0.82:\n      Successfully uninstalled opencv-python-headless-4.10.0.82\n  Attempting uninstall: idna\n    Found existing installation: idna 3.6\n    Uninstalling idna-3.6:\n      Successfully uninstalled idna-3.6\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\ntensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\njupyterlab 4.2.1 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\njupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\ntensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.3.3 which is incompatible.\nydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed chardet-4.0.0 idna-3.7 opencv-python-headless-4.10.0.84 python-magic-0.4.27 roboflow-1.1.33\nloading Roboflow workspace...\nloading Roboflow project...\nDependency ultralytics==8.0.196 is required but found version=8.2.24, to fix: `pip install ultralytics==8.0.196`\n","output_type":"stream"},{"name":"stderr","text":"Downloading Dataset Version Zip in surgical_tool_segmentation_mj-6 to yolov8:: 100%|██████████| 102541/102541 [00:02<00:00, 47039.02it/s]","output_type":"stream"},{"name":"stdout","text":"\n","output_type":"stream"},{"name":"stderr","text":"\nExtracting Dataset Version Zip to surgical_tool_segmentation_mj-6 in yolov8:: 100%|██████████| 8187/8187 [00:00<00:00, 9000.18it/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"results = model.train(data=\"/kaggle/input/dataset213/data.yaml\", epochs=10, imgsz=640)","metadata":{"execution":{"iopub.status.busy":"2024-06-21T08:56:53.119992Z","iopub.execute_input":"2024-06-21T08:56:53.120476Z","iopub.status.idle":"2024-06-21T09:10:53.747457Z","shell.execute_reply.started":"2024-06-21T08:56:53.120437Z","shell.execute_reply":"2024-06-21T09:10:53.746534Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"New https://pypi.org/project/ultralytics/8.2.38 available 😃 Update with 'pip install -U ultralytics'\nUltralytics YOLOv8.2.24 🚀 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla T4, 15102MiB)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8s-seg.pt, data=/kaggle/input/dataset213/data.yaml, epochs=10, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train5, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train5\n\n                   from  n    params  module                                       arguments                     \n  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]             \n  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]           \n  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, True]           \n  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 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    591360  ultralytics.nn.modules.block.C2f             [768, 256, 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    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]                 \n 22        [15, 18, 21]  1   2776349  ultralytics.nn.modules.head.Segment          [15, 32, 128, [128, 256, 512]]\nYOLOv8s-seg summary: 261 layers, 11795901 parameters, 11795885 gradients, 42.7 GFLOPs\n\nTransferred 417/417 items from pretrained weights\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train5', view at http://localhost:6006/\nFreezing layer 'model.22.dfl.conv.weight'\n\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/input/dataset213/train/labels... 3164 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3164/3164 [00:03<00:00, 885.01it/s]\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ Cache directory /kaggle/input/dataset213/train is not writeable, cache not saved.\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","output_type":"stream"},{"name":"stderr","text":"os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/input/dataset213/valid/labels... 925 images, 0 backgrounds, 0 corrupt: 100%|██████████| 925/925 [00:01<00:00, 800.08it/s]","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /kaggle/input/dataset213/valid/images/senn_s_mp4-0132_jpg.rf.0679086332fba04adcc7a574ce9943e0.jpg: 1 duplicate labels removed\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ Cache directory /kaggle/input/dataset213/valid is not writeable, cache not saved.\nPlotting labels to runs/segment/train5/labels.jpg... \n\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=0.000526, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\nImage sizes 640 train, 640 val\nUsing 2 dataloader workers\nLogging results to \u001b[1mruns/segment/train5\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","output_type":"stream"},{"name":"stderr","text":"os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\nos.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       1/10      9.37G     0.4732     0.6074       2.36     0.9395         13        640: 100%|██████████| 198/198 [01:09<00:00,  2.85it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:12<00:00,  2.39it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.346      0.656      0.444      0.376      0.347      0.653      0.447      0.321\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       2/10      9.13G     0.5509     0.5936      1.714     0.9636         13        640: 100%|██████████| 198/198 [01:07<00:00,  2.93it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.51it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.417      0.645      0.493      0.408      0.419      0.646      0.498      0.344\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       3/10      9.08G     0.5778     0.6021      1.534     0.9652         15        640: 100%|██████████| 198/198 [01:06<00:00,  2.96it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.53it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.407      0.628      0.514      0.429      0.405      0.629      0.516       0.37\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       4/10      9.12G     0.5351     0.5541      1.346     0.9484         15        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.52it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.485      0.676      0.592        0.5      0.483      0.681      0.598      0.435\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       5/10      9.12G     0.4867     0.5231      1.202     0.9165         18        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.56it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.576      0.692      0.689      0.604      0.576      0.692      0.689      0.524\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       6/10       9.1G     0.4332     0.4837     0.9966      0.897         12        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.54it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164        0.6      0.789      0.758      0.684      0.602      0.793       0.76      0.583\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       7/10      9.11G     0.3921      0.467     0.8763     0.8756         12        640: 100%|██████████| 198/198 [01:06<00:00,  2.98it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.53it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164       0.76       0.78      0.834      0.771      0.761      0.781      0.834      0.665\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       8/10       9.1G     0.3552     0.4365       0.76     0.8649         13        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.55it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164       0.78      0.785      0.864      0.816      0.785      0.784      0.865      0.698\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       9/10      9.11G     0.3155      0.406     0.6631      0.849         13        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.56it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.778      0.877      0.902      0.859      0.778      0.877      0.903       0.74\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      10/10       9.1G     0.2851      0.402     0.5566      0.829         15        640: 100%|██████████| 198/198 [01:06<00:00,  2.97it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:11<00:00,  2.53it/s]","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.861      0.853      0.921      0.885      0.861      0.853      0.922      0.763\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n10 epochs completed in 0.223 hours.\nOptimizer stripped from runs/segment/train5/weights/last.pt, 23.9MB\nOptimizer stripped from runs/segment/train5/weights/best.pt, 23.9MB\n\nValidating runs/segment/train5/weights/best.pt...\nUltralytics YOLOv8.2.24 🚀 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla T4, 15102MiB)\nYOLOv8s-seg summary (fused): 195 layers, 11785405 parameters, 0 gradients, 42.5 GFLOPs\n","output_type":"stream"},{"name":"stderr","text":"                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 29/29 [00:12<00:00,  2.24it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        925       1164      0.861      0.853      0.921      0.885      0.861      0.853      0.922      0.763\n          clamp_Aillis        925         60       0.85      0.848      0.936      0.917       0.85      0.848      0.936      0.802\n           clamp_kelly        925         82      0.731      0.963      0.936      0.932      0.731      0.963      0.936      0.761\n        clamp_mosqutio        925         96      0.942      0.843      0.913      0.888      0.942      0.843      0.913      0.761\n          clamp_sponge        925        100      0.936      0.883      0.975      0.957      0.936      0.883      0.975      0.808\n          forceps_long        925         91      0.943      0.846      0.964      0.874      0.943      0.846      0.964      0.724\n          forceps_wide        925         82      0.952       0.96      0.978      0.895      0.952       0.96      0.978       0.79\n      needle_holder_14        925         98      0.837      0.785      0.867      0.848      0.837      0.785      0.867      0.739\n      needle_holder_20        925         92      0.807      0.913       0.93      0.904      0.807      0.913       0.93      0.802\n                 punch        925         61      0.968      0.995      0.979      0.921      0.968      0.995      0.988      0.799\n        retractor_army        925         65      0.966      0.862      0.935      0.882      0.966      0.862      0.935      0.781\n      retractor_senn_b        925         62      0.872      0.657      0.891      0.853      0.872      0.657      0.891      0.683\n      retractor_senn_s        925         60      0.727      0.833      0.822      0.787      0.727      0.833      0.824      0.623\n          scissor_mayo        925         94      0.801      0.809       0.92      0.906      0.801      0.809       0.92      0.795\n    scissor_metzenbaum        925         63      0.813      0.873      0.923      0.894      0.813      0.873      0.923      0.803\n     scissor_operating        925         58      0.776      0.724       0.85       0.82      0.776      0.724       0.85      0.773\nSpeed: 0.3ms preprocess, 5.8ms inference, 0.0ms loss, 1.2ms postprocess per image\nResults saved to \u001b[1mruns/segment/train5\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}