kaggle_notebooks/prepare-and-train-small-dataset-yolov8.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":61446,"databundleVersionId":6962461,"sourceType":"competition"}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import glob\nimport json\nimport os\nimport cv2\nimport yaml\nimport shutil","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-10T10:50:51.954504Z","iopub.execute_input":"2024-01-10T10:50:51.954912Z","iopub.status.idle":"2024-01-10T10:50:51.959760Z","shell.execute_reply.started":"2024-01-10T10:50:51.954882Z","shell.execute_reply":"2024-01-10T10:50:51.958751Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# Convert tif labels to coco json format","metadata":{}},{"cell_type":"code","source":"MASK_EXT = 'tif'\nORIGINAL_EXT = 'tif'\nMASK_PATH = 'labels'\nIMG_PATH = 'images'","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:50:51.961340Z","iopub.execute_input":"2024-01-10T10:50:51.961623Z","iopub.status.idle":"2024-01-10T10:50:51.976817Z","shell.execute_reply.started":"2024-01-10T10:50:51.961599Z","shell.execute_reply":"2024-01-10T10:50:51.975912Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"def create_annotation_for_contour(contour, annotation_id: int, image_id):\n    bbox = cv2.boundingRect(contour)\n    area = cv2.contourArea(contour)\n    segmentation = contour.flatten().tolist()\n\n    annotation = {\n        \"iscrowd\": 0,\n        \"id\": annotation_id,\n        \"image_id\": image_id,\n        \"category_id\": 1,\n        \"bbox\": bbox,\n        \"area\": area,\n        \"segmentation\": [segmentation],\n    }\n\n    return annotation\n\n\ndef contours_from_mask_image(mask_image_open):\n    # Find contours in the mask image\n    gray = cv2.cvtColor(mask_image_open, cv2.COLOR_BGR2GRAY)\n    _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n    contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]\n    return contours\n\n\ndef images_annotations_info(path):\n    \"\"\"\n    Process the binary masks and generate images and annotations information.\n\n    :param path: Path to the directory containing images and binary masks\n    :return: Tuple containing images info, annotations info, and annotation count\n    \"\"\"\n    global image_id, annotation_id\n    annotations = []\n    images = []\n\n\n    for mask_image in glob.glob(os.path.join(path, MASK_PATH, f'*.{MASK_EXT}')):\n        original_file_name = f'{os.path.basename(mask_image).split(\".\")[0]}.{ORIGINAL_EXT}'\n        mask_image_open = cv2.imread(mask_image)\n\n        # Get image dimensions\n        height, width, _ = mask_image_open.shape\n\n        # Create or find existing image annotation\n        if original_file_name not in map(lambda img: img['file_name'], images):\n            image = {\n                \"id\": image_id + 1,\n                \"width\": width,\n                \"height\": height,\n                \"file_name\": original_file_name,\n            }\n            images.append(image)\n            image_id += 1\n        else:\n            image = [element for element in images if element['file_name'] == original_file_name][0]\n\n        contours = contours_from_mask_image(mask_image_open)\n\n        # Create annotation for each contour\n        for contour in contours:\n            annotation = create_annotation_for_contour(contour, annotation_id, image['id'])\n\n            # Add annotation if area is greater than zero\n            if annotation[\"area\"] > 0:\n                annotations.append(annotation)\n                annotation_id += 1\n\n    return images, annotations, annotation_id\n\n\ndef process_masks(mask_path, dest_json):\n    # Initialize the COCO JSON format with categories\n    coco_format = {\n        \"info\": {},\n        \"licenses\": [],\n        \"images\": [],\n        \"categories\": [{\"id\": 1, \"name\": 'Vessel', \"supercategory\": 'Vessel'}],\n        \"annotations\": [],\n    }\n\n    # Create images and annotations sections\n    coco_format[\"images\"], coco_format[\"annotations\"], annotation_cnt = images_annotations_info(mask_path)\n\n    # Save the COCO JSON to a file\n    with open(dest_json, \"w\") as outfile:\n        json.dump(coco_format, outfile, sort_keys=True, indent=4)\n\n    print(f\"Created {annotation_cnt} annotations for images in folder: {mask_path}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:50:52.056218Z","iopub.execute_input":"2024-01-10T10:50:52.056481Z","iopub.status.idle":"2024-01-10T10:50:52.071295Z","shell.execute_reply.started":"2024-01-10T10:50:52.056459Z","shell.execute_reply":"2024-01-10T10:50:52.070368Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"!mkdir -p small/train/images\n!mkdir -p small/val/images\n\n!mkdir -p small/train/labels\n!mkdir -p small/val/labels","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:50:52.072923Z","iopub.execute_input":"2024-01-10T10:50:52.073233Z","iopub.status.idle":"2024-01-10T10:50:55.995095Z","shell.execute_reply.started":"2024-01-10T10:50:52.073209Z","shell.execute_reply":"2024-01-10T10:50:55.993984Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# copy images 0500.tif - 0599.tif\n!cp /kaggle/input/blood-vessel-segmentation/train/kidney_1_dense/images/05**.tif /kaggle/working/small/train/images\n!cp /kaggle/input/blood-vessel-segmentation/train/kidney_3_sparse/images/05**.tif /kaggle/working/small/val/images\n","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:50:55.996738Z","iopub.execute_input":"2024-01-10T10:50:55.997143Z","iopub.status.idle":"2024-01-10T10:51:04.921006Z","shell.execute_reply.started":"2024-01-10T10:50:55.997090Z","shell.execute_reply":"2024-01-10T10:51:04.919703Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# copy labels 0500.tif - 0599.tif\n!cp /kaggle/input/blood-vessel-segmentation/train/kidney_1_dense/labels/05**.tif /kaggle/working/small/train/labels\n!cp /kaggle/input/blood-vessel-segmentation/train/kidney_3_dense/labels/05**.tif /kaggle/working/small/val/labels\n","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:04.922457Z","iopub.execute_input":"2024-01-10T10:51:04.922746Z","iopub.status.idle":"2024-01-10T10:51:09.746350Z","shell.execute_reply.started":"2024-01-10T10:51:04.922720Z","shell.execute_reply":"2024-01-10T10:51:09.744976Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Sanity check to see if we have 100 files in each dir\n!ls -l small/val/labels | grep \"^-\" | wc -l\n!ls -l small/val/images | grep \"^-\" | wc -l\n!ls -l small/train/labels | grep \"^-\" | wc -l\n!ls -l small/train/images | grep \"^-\" | wc -l\n","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:09.749703Z","iopub.execute_input":"2024-01-10T10:51:09.750012Z","iopub.status.idle":"2024-01-10T10:51:13.690250Z","shell.execute_reply.started":"2024-01-10T10:51:09.749985Z","shell.execute_reply":"2024-01-10T10:51:13.688926Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"100\n100\n100\n100\n","output_type":"stream"}]},{"cell_type":"code","source":"global image_id, annotation_id\nimage_id = 0\nannotation_id = 0","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:13.692042Z","iopub.execute_input":"2024-01-10T10:51:13.692495Z","iopub.status.idle":"2024-01-10T10:51:13.698162Z","shell.execute_reply.started":"2024-01-10T10:51:13.692453Z","shell.execute_reply":"2024-01-10T10:51:13.697184Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"train_path = '/kaggle/working/small/train'\nval_path = '/kaggle/working/small/val'\n\ntrain_json_path = '/kaggle/working/small/train/train.json'\nval_json_path = '/kaggle/working/small/val/val.json'","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:13.699527Z","iopub.execute_input":"2024-01-10T10:51:13.699845Z","iopub.status.idle":"2024-01-10T10:51:13.710188Z","shell.execute_reply.started":"2024-01-10T10:51:13.699818Z","shell.execute_reply":"2024-01-10T10:51:13.709156Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"process_masks(train_path, train_json_path)\nprocess_masks(val_path, val_json_path)","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:13.711524Z","iopub.execute_input":"2024-01-10T10:51:13.711807Z","iopub.status.idle":"2024-01-10T10:51:18.263088Z","shell.execute_reply.started":"2024-01-10T10:51:13.711783Z","shell.execute_reply":"2024-01-10T10:51:18.262164Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Created 10959 annotations for images in folder: /kaggle/working/small/train\nCreated 21024 annotations for images in folder: /kaggle/working/small/val\n","output_type":"stream"}]},{"cell_type":"code","source":"!rm /kaggle/working/small/train/labels/*.tif\n!rm /kaggle/working/small/val/labels/*.tif","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:18.264345Z","iopub.execute_input":"2024-01-10T10:51:18.264631Z","iopub.status.idle":"2024-01-10T10:51:20.257281Z","shell.execute_reply.started":"2024-01-10T10:51:18.264606Z","shell.execute_reply":"2024-01-10T10:51:20.256108Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"!tail -n 100 /kaggle/working/small/val/val.json","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:20.258824Z","iopub.execute_input":"2024-01-10T10:51:20.259185Z","iopub.status.idle":"2024-01-10T10:51:21.248647Z","shell.execute_reply.started":"2024-01-10T10:51:20.259142Z","shell.execute_reply":"2024-01-10T10:51:21.247447Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"        {\n            \"file_name\": \"0589.tif\",\n            \"height\": 1706,\n            \"id\": 185,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0507.tif\",\n            \"height\": 1706,\n            \"id\": 186,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0593.tif\",\n            \"height\": 1706,\n            \"id\": 187,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0500.tif\",\n            \"height\": 1706,\n            \"id\": 188,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0505.tif\",\n            \"height\": 1706,\n            \"id\": 189,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0533.tif\",\n            \"height\": 1706,\n            \"id\": 190,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0539.tif\",\n            \"height\": 1706,\n            \"id\": 191,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0587.tif\",\n            \"height\": 1706,\n            \"id\": 192,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0590.tif\",\n            \"height\": 1706,\n            \"id\": 193,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0504.tif\",\n            \"height\": 1706,\n            \"id\": 194,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0596.tif\",\n            \"height\": 1706,\n            \"id\": 195,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0528.tif\",\n            \"height\": 1706,\n            \"id\": 196,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0575.tif\",\n            \"height\": 1706,\n            \"id\": 197,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0545.tif\",\n            \"height\": 1706,\n            \"id\": 198,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0555.tif\",\n            \"height\": 1706,\n            \"id\": 199,\n            \"width\": 1510\n        },\n        {\n            \"file_name\": \"0540.tif\",\n            \"height\": 1706,\n            \"id\": 200,\n            \"width\": 1510\n        }\n    ],\n    \"info\": {},\n    \"licenses\": []\n}","output_type":"stream"}]},{"cell_type":"markdown","source":"# Convert annotations from coco json to yolo format using ultralytics `convert_coco`","metadata":{}},{"cell_type":"code","source":"!pip install ultralytics","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:21.250216Z","iopub.execute_input":"2024-01-10T10:51:21.250549Z","iopub.status.idle":"2024-01-10T10:51:37.167106Z","shell.execute_reply.started":"2024-01-10T10:51:21.250521Z","shell.execute_reply":"2024-01-10T10:51:37.165934Z"},"_kg_hide-output":true,"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Collecting ultralytics\n  Obtaining dependency information for ultralytics from https://files.pythonhosted.org/packages/57/2e/9d58b392f3cb41f75e1f8e274e528c48c46e47caf581e3e34147780c26dc/ultralytics-8.1.0-py3-none-any.whl.metadata\n  Downloading ultralytics-8.1.0-py3-none-any.whl.metadata (39 kB)\nRequirement already satisfied: matplotlib>=3.3.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (3.7.4)\nRequirement already satisfied: numpy>=1.22.2 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (1.24.3)\nRequirement already satisfied: opencv-python>=4.6.0 in 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(5.9.3)\nRequirement already satisfied: py-cpuinfo in /opt/conda/lib/python3.10/site-packages (from ultralytics) (9.0.0)\nCollecting thop>=0.1.1 (from ultralytics)\n  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\nRequirement already satisfied: pandas>=1.1.4 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (2.0.3)\nRequirement already satisfied: seaborn>=0.11.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (0.12.2)\nCollecting hub-sdk>=0.0.2 (from ultralytics)\n  Obtaining dependency information for hub-sdk>=0.0.2 from https://files.pythonhosted.org/packages/f1/ef/940bd48e9d0fc93109b77f944a86729fbb1e08bbcbb4f558988ccebe853b/hub_sdk-0.0.2-py3-none-any.whl.metadata\n  Downloading hub_sdk-0.0.2-py3-none-any.whl.metadata (8.8 kB)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in 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hub_sdk-0.0.2-py3-none-any.whl (37 kB)\nInstalling collected packages: hub-sdk, thop, ultralytics\nSuccessfully installed hub-sdk-0.0.2 thop-0.1.1.post2209072238 ultralytics-8.1.0\n","output_type":"stream"}]},{"cell_type":"code","source":"from ultralytics.data.converter import convert_coco","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:37.168814Z","iopub.execute_input":"2024-01-10T10:51:37.169445Z","iopub.status.idle":"2024-01-10T10:51:41.723948Z","shell.execute_reply.started":"2024-01-10T10:51:37.169405Z","shell.execute_reply":"2024-01-10T10:51:41.723127Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"convert_coco(labels_dir='/kaggle/working/small/train', \n             use_segments=True,\n             cls91to80=False,)","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:41.727201Z","iopub.execute_input":"2024-01-10T10:51:41.727641Z","iopub.status.idle":"2024-01-10T10:51:42.527157Z","shell.execute_reply.started":"2024-01-10T10:51:41.727612Z","shell.execute_reply":"2024-01-10T10:51:42.526307Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"Annotations /kaggle/working/small/train/train.json: 100%|██████████| 100/100 [00:00<00:00, 182.53it/s]","output_type":"stream"},{"name":"stdout","text":"COCO data converted successfully.\nResults saved to /kaggle/working/coco_converted\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"!cp  /kaggle/working/coco_converted/labels/train/*.txt /kaggle/working/small/train/labels/\n!rm -r /kaggle/working/coco_converted","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:42.528275Z","iopub.execute_input":"2024-01-10T10:51:42.528572Z","iopub.status.idle":"2024-01-10T10:51:44.500042Z","shell.execute_reply.started":"2024-01-10T10:51:42.528547Z","shell.execute_reply":"2024-01-10T10:51:44.498709Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"convert_coco(labels_dir='/kaggle/working/small/val', \n             use_segments=True,\n             cls91to80=False,)","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:44.502001Z","iopub.execute_input":"2024-01-10T10:51:44.502395Z","iopub.status.idle":"2024-01-10T10:51:45.195973Z","shell.execute_reply.started":"2024-01-10T10:51:44.502364Z","shell.execute_reply":"2024-01-10T10:51:45.194961Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Annotations /kaggle/working/small/val/val.json: 100%|██████████| 100/100 [00:00<00:00, 183.70it/s]","output_type":"stream"},{"name":"stdout","text":"COCO data converted successfully.\nResults saved to /kaggle/working/coco_converted\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"!cp  /kaggle/working/coco_converted/labels/val/*.txt /kaggle/working/small/val/labels/\n!rm -r /kaggle/working/coco_converted","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:45.197187Z","iopub.execute_input":"2024-01-10T10:51:45.197457Z","iopub.status.idle":"2024-01-10T10:51:47.149034Z","shell.execute_reply.started":"2024-01-10T10:51:45.197432Z","shell.execute_reply":"2024-01-10T10:51:47.147873Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"# Write yolo dataset YAML","metadata":{}},{"cell_type":"code","source":"names = {0: 'Vessel'}\n\n# Number of classes\nnc = len(names)\n\n# Create a dictionary with the required content\nyaml_data = {\n    'names': names,\n    'nc': nc,\n    'test': '',\n    'train': train_path,\n    'val': val_path\n}\n\n# Write the dictionary to a YAML file\nwith open('small/small_yolo.yaml', 'w') as file:\n    yaml.dump(yaml_data, file, default_flow_style=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:47.150519Z","iopub.execute_input":"2024-01-10T10:51:47.150822Z","iopub.status.idle":"2024-01-10T10:51:47.158414Z","shell.execute_reply.started":"2024-01-10T10:51:47.150786Z","shell.execute_reply":"2024-01-10T10:51:47.157514Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"!cat small/small_yolo.yaml","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:47.159537Z","iopub.execute_input":"2024-01-10T10:51:47.159802Z","iopub.status.idle":"2024-01-10T10:51:48.184843Z","shell.execute_reply.started":"2024-01-10T10:51:47.159779Z","shell.execute_reply":"2024-01-10T10:51:48.183612Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"names:\n  0: Vessel\nnc: 1\ntest: ''\ntrain: /kaggle/working/small/train\nval: /kaggle/working/small/val\n","output_type":"stream"}]},{"cell_type":"code","source":"!head /kaggle/working/small/train/labels/0582.txt","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:48.186436Z","iopub.execute_input":"2024-01-10T10:51:48.186765Z","iopub.status.idle":"2024-01-10T10:51:49.206244Z","shell.execute_reply.started":"2024-01-10T10:51:48.186738Z","shell.execute_reply":"2024-01-10T10:51:49.205180Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"0 0.265351 0.853415 0.265351 0.854183 0.266447 0.85495 0.267544 0.854183 0.266447 0.853415\n0 0.257675 0.84881 0.258772 0.849578 0.259868 0.849578 0.259868 0.84881\n0 0.308114 0.838066 0.309211 0.838833 0.310307 0.838833 0.310307 0.838066\n0 0.309211 0.827322 0.308114 0.828089 0.308114 0.830391 0.309211 0.830391 0.309211 0.829624 0.311404 0.828089 0.310307 0.827322\n0 0.198465 0.822717 0.196272 0.824252 0.196272 0.825787 0.195175 0.826554 0.195175 0.829624 0.197368 0.828089 0.197368 0.826554 0.198465 0.825787\n0 0.253289 0.81581 0.253289 0.816577 0.254386 0.816577 0.254386 0.81581\n0 0.343202 0.815042 0.343202 0.818112 0.344298 0.818112 0.345395 0.81888 0.346491 0.81888 0.345395 0.818112 0.345395 0.816577\n0 0.216009 0.801995 0.213816 0.80353 0.213816 0.805833 0.211623 0.807368 0.212719 0.808135 0.213816 0.807368 0.214912 0.807368 0.216009 0.8066 0.216009 0.805833 0.217105 0.805065 0.217105 0.801995\n0 0.195175 0.795088 0.195175 0.795856 0.196272 0.795088\n0 0.226974 0.792786 0.225877 0.793553 0.225877 0.795856 0.226974 0.796623 0.22807 0.796623 0.22807 0.795856 0.229167 0.795088 0.229167 0.793553 0.22807 0.792786\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Train yolov8 model","metadata":{}},{"cell_type":"code","source":"project = 'hacking_human_vasculature_small'\nname = 'small_set'","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:51:49.208032Z","iopub.execute_input":"2024-01-10T10:51:49.208490Z","iopub.status.idle":"2024-01-10T10:51:49.214167Z","shell.execute_reply.started":"2024-01-10T10:51:49.208452Z","shell.execute_reply":"2024-01-10T10:51:49.213085Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"!pip install -U ipywidgets","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:53:00.224937Z","iopub.execute_input":"2024-01-10T10:53:00.225330Z","iopub.status.idle":"2024-01-10T10:53:14.208317Z","shell.execute_reply.started":"2024-01-10T10:53:00.225299Z","shell.execute_reply":"2024-01-10T10:53:14.207038Z"},"_kg_hide-output":true,"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Requirement already satisfied: ipywidgets in /opt/conda/lib/python3.10/site-packages (7.7.1)\nCollecting ipywidgets\n  Obtaining dependency information for ipywidgets from https://files.pythonhosted.org/packages/4a/0e/57ed498fafbc60419a9332d872e929879ceba2d73cb11d284d7112472b3e/ipywidgets-8.1.1-py3-none-any.whl.metadata\n  Downloading ipywidgets-8.1.1-py3-none-any.whl.metadata (2.4 kB)\nRequirement already satisfied: comm>=0.1.3 in /opt/conda/lib/python3.10/site-packages (from ipywidgets) (0.1.4)\nRequirement already satisfied: ipython>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from ipywidgets) (8.14.0)\nRequirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.10/site-packages (from ipywidgets) (5.9.0)\nCollecting widgetsnbextension~=4.0.9 (from ipywidgets)\n  Obtaining dependency information for widgetsnbextension~=4.0.9 from https://files.pythonhosted.org/packages/29/03/107d96077c4befed191f7ad1a12c7b52a8f9d2778a5836d59f9855c105f6/widgetsnbextension-4.0.9-py3-none-any.whl.metadata\n  Downloading widgetsnbextension-4.0.9-py3-none-any.whl.metadata (1.6 kB)\nCollecting jupyterlab-widgets~=3.0.9 (from ipywidgets)\n  Obtaining dependency information for jupyterlab-widgets~=3.0.9 from https://files.pythonhosted.org/packages/e8/05/0ebab152288693b5ec7b339aab857362947031143b282853b4c2dd4b5b40/jupyterlab_widgets-3.0.9-py3-none-any.whl.metadata\n  Downloading jupyterlab_widgets-3.0.9-py3-none-any.whl.metadata (4.1 kB)\nRequirement already satisfied: backcall in /opt/conda/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.2.0)\nRequirement already satisfied: decorator in /opt/conda/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\nRequirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.0)\nRequirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\nRequirement already satisfied: pickleshare in /opt/conda/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.7.5)\nRequirement already satisfied: 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uninstalled ipywidgets-7.7.1\nSuccessfully installed ipywidgets-8.1.1 jupyterlab-widgets-3.0.9 widgetsnbextension-4.0.9\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Enable wandb logging","metadata":{}},{"cell_type":"code","source":"import wandb\nfrom kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nwandb_api_key = user_secrets.get_secret(\"wandb-api-key\")\nwandb.login(key=wandb_api_key)","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:54:32.425413Z","iopub.execute_input":"2024-01-10T10:54:32.426353Z","iopub.status.idle":"2024-01-10T10:54:50.709902Z","shell.execute_reply.started":"2024-01-10T10:54:32.426309Z","shell.execute_reply":"2024-01-10T10:54:50.708923Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"  ········································\n"},{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}]},{"cell_type":"markdown","source":"## Load segmentation model weights from pretrained and train on the small set","metadata":{}},{"cell_type":"code","source":"from ultralytics import YOLO\n\nmodel = YOLO('yolov8n-seg.pt')","metadata":{"execution":{"iopub.status.busy":"2024-01-10T10:54:57.348486Z","iopub.execute_input":"2024-01-10T10:54:57.349254Z","iopub.status.idle":"2024-01-10T10:54:57.701601Z","shell.execute_reply.started":"2024-01-10T10:54:57.349219Z","shell.execute_reply":"2024-01-10T10:54:57.700569Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"\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   1150432  ultralytics.nn.modules.head.Segment          [80, 32, 64, [64, 128, 256]]  \nYOLOv8n-seg summary: 261 layers, 3409968 parameters, 3409952 gradients, 12.8 GFLOPs\n\n","output_type":"stream"}]},{"cell_type":"code","source":"results = model.train(data='small/small_yolo.yaml',\n                      project=project,\n                      name=name,\n                      epochs=200,\n                      patience=0, #I am setting patience=0 to disable early stopping.\n                      batch=4,\n                      imgsz=1706,\n                      device=[0, 1]\n                     )","metadata":{"execution":{"iopub.status.busy":"2024-01-10T11:07:59.223020Z","iopub.execute_input":"2024-01-10T11:07:59.223464Z","iopub.status.idle":"2024-01-10T11:11:31.757641Z","shell.execute_reply.started":"2024-01-10T11:07:59.223432Z","shell.execute_reply":"2024-01-10T11:11:31.753514Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"Ultralytics YOLOv8.1.0 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla T4, 15102MiB)\n                                                     CUDA:1 (Tesla T4, 15102MiB)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8n-seg.pt, data=small/small_yolo.yaml, epochs=200, time=None, patience=0, batch=4, imgsz=1706, save=True, save_period=-1, cache=False, device=[0, 1], workers=8, project=hacking_human_vasculature_small, name=small_set3, 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=0.0, 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, mosaic=0.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=hacking_human_vasculature_small/small_set3\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   1004275  ultralytics.nn.modules.head.Segment          [1, 32, 64, [64, 128, 256]]   \nYOLOv8n-seg summary: 261 layers, 3263811 parameters, 3263795 gradients, 12.1 GFLOPs\n\nTransferred 417/417 items from pretrained weights\n\u001b[34m\u001b[1mDDP:\u001b[0m debug command /opt/conda/bin/python3.10 -m torch.distributed.run --nproc_per_node 2 --master_port 58147 /root/.config/Ultralytics/DDP/_temp_htz368w8136723643690304.py\nUltralytics YOLOv8.1.0 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla T4, 15102MiB)\n                                                     CUDA:1 (Tesla T4, 15102MiB)\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir hacking_human_vasculature_small/small_set3', view at http://localhost:6006/\n","output_type":"stream"},{"name":"stderr","text":"wandb: Currently logged in as: aaalex-lit. Use `wandb login --relogin` to force relogin\nwandb: wandb version 0.16.2 is available!  To upgrade, please run:\nwandb:  $ pip install wandb --upgrade\nwandb: Tracking run with wandb version 0.16.1\nwandb: Run data is saved locally in /kaggle/working/wandb/run-20240110_110810-ix1kp1lg\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run small_set3\nwandb: ⭐️ View project at https://wandb.ai/aaalex-lit/hacking_human_vasculature_small\nwandb: 🚀 View run at https://wandb.ai/aaalex-lit/hacking_human_vasculature_small/runs/ix1kp1lg\n","output_type":"stream"},{"name":"stdout","text":"Overriding model.yaml nc=80 with nc=1\nTransferred 381/417 items from pretrained weights\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 ✅\nWARNING ⚠️ imgsz=[1706] must be multiple of max stride 32, updating to [1728]\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/small/train/labels.cache... 100 images, 0 backgrounds, 0 corrupt: 100%|██████████| 100/100 [00:00<?, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"\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":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/small/val/labels.cache... 100 images, 0 backgrounds, 0 corrupt: 100%|██████████| 100/100 [00:00<?, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"Plotting labels to hacking_human_vasculature_small/small_set3/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=0.000714, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n200 epochs...\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      1/200      8.55G       2.92      4.837      5.184      1.561        159       1728: 100%|██████████| 25/25 [00:16<00:00,  1.51it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:09<00:00,  2.55it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064          0          0          0          0          0          0          0          0\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      2/200      8.62G      1.901      2.535      3.318     0.9452        155       1728: 100%|██████████| 25/25 [00:10<00:00,  2.41it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:08<00:00,  2.90it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064    0.00557     0.0166    0.00632    0.00433    0.00507     0.0151    0.00584    0.00341\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      3/200       7.9G      1.566      1.916      2.241     0.8867        154       1728: 100%|██████████| 25/25 [00:10<00:00,  2.31it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:09<00:00,  2.76it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064     0.0136     0.0405     0.0132    0.00952     0.0131      0.039     0.0126    0.00592\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      4/200      5.62G      1.533      1.655      2.074     0.8766        192       1728: 100%|██████████| 25/25 [00:09<00:00,  2.53it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:09<00:00,  2.68it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064     0.0513      0.153      0.071      0.047     0.0487      0.145      0.065     0.0318\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      5/200      10.4G       1.56      1.726      2.122     0.8747        197       1728: 100%|██████████| 25/25 [00:10<00:00,  2.39it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:12<00:00,  1.93it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064      0.768      0.212      0.317      0.166      0.695      0.192      0.265      0.113\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      6/200      13.2G      1.483      1.643      1.872     0.8737        170       1728: 100%|██████████| 25/25 [00:11<00:00,  2.25it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 25/25 [00:20<00:00,  1.22it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all        100      10064      0.748      0.274      0.355      0.193      0.673      0.242      0.308      0.136\n\n      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      7/200      8.65G      1.393      1.547      1.658     0.8746        182       1728:  52%|█████▏    | 13/25 [00:05<00:05,  2.21it/s]Traceback (most recent call last):\n  File \"/opt/conda/lib/python3.10/site-packages/tqdm/std.py\", line 1182, in __iter__\n    for obj in iterable:\n  File \"/opt/conda/lib/python3.10/site-packages/ultralytics/data/build.py\", line 50, in __iter__\n    yield next(self.iterator)\n  File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 634, in __next__\n    data = self._next_data()\n  File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1329, in _next_data\n    idx, data = self._get_data()\n  File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1285, in _get_data\n    success, data = self._try_get_data()\n  File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1133, in _try_get_data\n    data = self._data_queue.get(timeout=timeout)\n  File \"/opt/conda/lib/python3.10/queue.py\", line 180, in get\n    self.not_empty.wait(remaining)\n  File \"/opt/conda/lib/python3.10/threading.py\", line 324, in wait\n    gotit = waiter.acquire(True, timeout)\nKeyboardInterrupt\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/root/.config/Ultralytics/DDP/_temp_htz368w8136723643690304.py\", line 12, in <module>\n    results = trainer.train()\n  File \"/opt/conda/lib/python3.10/site-packages/ultralytics/engine/trainer.py\", line 208, in train\n    self._do_train(world_size)\n  File \"/opt/conda/lib/python3.10/site-packages/ultralytics/engine/trainer.py\", line 361, in _do_train\n    for i, batch in pbar:\n  File \"/opt/conda/lib/python3.10/site-packages/tqdm/std.py\", line 1197, in __iter__\n    self.close()\n  File \"/opt/conda/lib/python3.10/site-packages/tqdm/std.py\", line 1291, in close\n    fp_write('')\n  File \"/opt/conda/lib/python3.10/site-packages/tqdm/std.py\", line 1288, in fp_write\n    self.fp.write(str(s))\n  File \"/opt/conda/lib/python3.10/site-packages/tqdm/utils.py\", line 195, in inner\n    return func(*args, **kwargs)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/lib/redirect.py\", line 643, in write\n    cb(data)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/wandb_run.py\", line 2163, in <lambda>\n    lambda data: self._console_raw_callback(\"stderr\", data),\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/wandb_run.py\", line 371, in wrapper_fn\n    return func(self, *args, **kwargs)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/wandb_run.py\", line 1493, in _console_raw_callback\n    self._backend.interface.publish_output_raw(name, data)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/interface/interface.py\", line 639, in publish_output_raw\n    self._publish_output_raw(o)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py\", line 79, in _publish_output_raw\n    self._publish(rec)\n  File \"/opt/conda/lib/python3.10/site-packages/wandb/sdk/interface/interface_sock.py\", line 50, in _publish\n    self._assign(record)\nKeyboardInterrupt\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msmall/small_yolo.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproject\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#I am setting patience=0 to disable early stopping.\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1706\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m                     \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/engine/model.py:390\u001b[0m, in \u001b[0;36mModel.train\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mhub_session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msession  \u001b[38;5;66;03m# attach optional HUB session\u001b[39;00m\n\u001b[0;32m--> 390\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    391\u001b[0m \u001b[38;5;66;03m# Update model and cfg after training\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ultralytics/engine/trainer.py:201\u001b[0m, in \u001b[0;36mBaseTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    200\u001b[0m     LOGGER\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolorstr(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDDP:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m debug command \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(cmd)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 201\u001b[0m     \u001b[43msubprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    203\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n","File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:505\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Popen(\u001b[38;5;241m*\u001b[39mpopenargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mas\u001b[39;00m process:\n\u001b[1;32m    504\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 505\u001b[0m         stdout, stderr \u001b[38;5;241m=\u001b[39m \u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommunicate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    506\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m TimeoutExpired \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    507\u001b[0m         process\u001b[38;5;241m.\u001b[39mkill()\n","File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1146\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1144\u001b[0m         stderr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1145\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1146\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1148\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1209\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     endtime \u001b[38;5;241m=\u001b[39m _time() \u001b[38;5;241m+\u001b[39m timeout\n\u001b[1;32m   1208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1209\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1211\u001b[0m     \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m   1212\u001b[0m     \u001b[38;5;66;03m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m   1213\u001b[0m     \u001b[38;5;66;03m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m   1214\u001b[0m     \u001b[38;5;66;03m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m   1215\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1959\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1958\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m  \u001b[38;5;66;03m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1959\u001b[0m (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1960\u001b[0m \u001b[38;5;66;03m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m   1961\u001b[0m \u001b[38;5;66;03m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m   1962\u001b[0m \u001b[38;5;66;03m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m   1963\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pid \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid:\n","File \u001b[0;32m/opt/conda/lib/python3.10/subprocess.py:1917\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m   1915\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m   1916\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1917\u001b[0m     (pid, sts) \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitpid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwait_flags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1918\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mChildProcessError\u001b[39;00m:\n\u001b[1;32m   1919\u001b[0m     \u001b[38;5;66;03m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m   1920\u001b[0m     \u001b[38;5;66;03m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m   1921\u001b[0m     \u001b[38;5;66;03m# process.  This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m   1922\u001b[0m     pid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"wandb.finish()","metadata":{"execution":{"iopub.status.busy":"2024-01-10T11:23:38.426869Z","iopub.execute_input":"2024-01-10T11:23:38.427311Z","iopub.status.idle":"2024-01-10T11:23:38.432705Z","shell.execute_reply.started":"2024-01-10T11:23:38.427278Z","shell.execute_reply":"2024-01-10T11:23:38.431653Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}