Model/Train_Model_NB_1.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":"gpu","dataSources":[{"sourceId":2727200,"sourceType":"datasetVersion","datasetId":1662330}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from tqdm.auto import tqdm\n\nimport shutil\nimport os\n\n\ndef load_img_path(load_data_path: str, img_path: str, labels_path: str) -> tuple:\n    img_paths, labels_paths = [], []\n    img, lbl = os.listdir(img_path), os.listdir(labels_path)\n    img.sort(); lbl.sort()\n\n    with open(load_data_path, 'r') as f:\n        for name in f:\n            name = name.strip()\n            name = name.replace('\\n', '') + '.jpg'\n            if name in img: img_paths.append(f'{img_path}/{name}')\n            else: continue\n\n            name = name.replace(\".jpg\", \".txt\")\n            if name in lbl: labels_paths.append(f'{labels_path}/{name}')\n            else: img_paths.remove(f'{img_path}/{name}') # Fail safe\n    \n    return (img_paths, labels_paths)\n\n\ndef process_data(mode: str, load_data_list: tuple, save_path: str) -> None:\n    img, label = load_img_path(*load_data_list)\n\n    output_paths = [save_path + f'/{mode}/images', save_path + f'/{mode}/labels']\n    for _path in output_paths:\n        if not os.path.isdir(_path):\n            os.makedirs(_path)\n    \n    for i in tqdm(range(len(img))):\n        image_path = img[i]\n        label_path = label[i]\n\n        shutil.copy(image_path, output_paths[0])\n        shutil.copy(label_path, output_paths[1])\n\n\ndata_path = '../input/diordata'\nsave_path = '/kaggle/temp/Model_Dataset'\n\ntrain_img_name_path = data_path + '/ImageSets/train.txt'\nval_img_name_path = data_path + '/ImageSets/val.txt'\ntest_img_name_path = data_path + '/ImageSets/test.txt'\n\nimg_path = data_path + '/images'\nlabels_path = data_path + '/labels'\n\n#Train\nprocess_data('train', (train_img_name_path, img_path, labels_path), save_path)\n# Val\nprocess_data('val', (val_img_name_path, img_path, labels_path), save_path)\n# Test\nprocess_data('test', (test_img_name_path, img_path, labels_path), save_path)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-15T10:56:29.178705Z","iopub.execute_input":"2023-12-15T10:56:29.179118Z","iopub.status.idle":"2023-12-15T11:02:11.914547Z","shell.execute_reply.started":"2023-12-15T10:56:29.179085Z","shell.execute_reply":"2023-12-15T11:02:11.913646Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/19004 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f8a7c04101ec46748c9b0224301f9a86"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/2112 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a946105748964ad4a26b4b5d603991b8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/2347 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b466fdfc0cbd4275942f654c8231e444"}},"metadata":{}}]},{"cell_type":"code","source":"!pip install ultralytics\n!pip install albumentations==1.0.3","metadata":{"execution":{"iopub.status.busy":"2023-12-15T11:02:36.227597Z","iopub.execute_input":"2023-12-15T11:02:36.228028Z","iopub.status.idle":"2023-12-15T11:03:01.731041Z","shell.execute_reply.started":"2023-12-15T11:02:36.227985Z","shell.execute_reply":"2023-12-15T11:03:01.729989Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Collecting ultralytics\n  Obtaining dependency information for ultralytics from https://files.pythonhosted.org/packages/f4/79/b192c64e13eefc7f8543a10080767ab057850fa9bdbc38e7c261a3432c93/ultralytics-8.0.227-py3-none-any.whl.metadata\n  Downloading ultralytics-8.0.227-py3-none-any.whl.metadata (32 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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/opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.3)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\nDownloading ultralytics-8.0.227-py3-none-any.whl (660 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m660.5/660.5 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: thop, ultralytics\nSuccessfully installed thop-0.1.1.post2209072238 ultralytics-8.0.227\nCollecting albumentations==1.0.3\n  Downloading albumentations-1.0.3-py3-none-any.whl (98 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta 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satisfied: imageio>=2.27 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16.1->albumentations==1.0.3) (2.31.1)\nRequirement already satisfied: tifffile>=2022.8.12 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16.1->albumentations==1.0.3) (2023.8.12)\nRequirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16.1->albumentations==1.0.3) (1.4.1)\nRequirement already satisfied: packaging>=21 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16.1->albumentations==1.0.3) (21.3)\nRequirement already satisfied: lazy_loader>=0.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.16.1->albumentations==1.0.3) (0.3)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=21->scikit-image>=0.16.1->albumentations==1.0.3) (3.0.9)\nInstalling collected packages: albumentations\n  Attempting uninstall: albumentations\n    Found existing installation: albumentations 1.3.1\n    Uninstalling albumentations-1.3.1:\n      Successfully uninstalled albumentations-1.3.1\nSuccessfully installed albumentations-1.0.3\n","output_type":"stream"}]},{"cell_type":"code","source":"from ultralytics import YOLO\n\nimport yaml\n\nmodel_data_path = '/kaggle/working/Model'\n\nmodel = YOLO('/kaggle/working/runs/detect/Yolov8/weights/best.pt')\nlabels = [\n    'Storage Tank',\n    'Baseball field',\n    'Tennis court',\n    'Basketball Court',\n    'Wind mill',\n    'Vehicle',\n    'Harbor',\n    'Ship',\n    'Airplane',\n    'Bridge',\n    'Overpass',\n    'Expressway toll station',\n    'Train station',\n    'Chimney',\n    'Ground Track Field',\n    'Dam',\n    'Expressway service area',\n    'Stadium',\n    'Airport',\n    'Golf course'\n]\nconfig = {\n    'path': '/kaggle/temp',\n    'train': 'Model_Dataset/train/images',\n    'val': 'Model_Dataset/val/images',\n    'test': 'Model_Dataset/test/images',\n    'names': {str(i): label for i, label in enumerate(labels)}\n}\n\nwith open(model_data_path + '\\config.yaml', 'w') as f:\n    yaml.dump(config, f, default_flow_style=False)\n\nmodel.train(data=model_data_path + '\\config.yaml', imgsz=800, epochs=20, batch=16, name='Yolov8_1')","metadata":{"execution":{"iopub.status.busy":"2023-12-15T11:03:15.662280Z","iopub.execute_input":"2023-12-15T11:03:15.663349Z","iopub.status.idle":"2023-12-15T13:12:16.248604Z","shell.execute_reply.started":"2023-12-15T11:03:15.663307Z","shell.execute_reply":"2023-12-15T13:12:16.247291Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Ultralytics YOLOv8.0.227 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=/kaggle/working/runs/detect/Yolov8/weights/best.pt, data=/kaggle/working/Model\\config.yaml, epochs=20, patience=50, batch=16, imgsz=800, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=Yolov8_1, 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, 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, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/Yolov8_1\nDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 755k/755k [00:00<00:00, 22.2MB/s]\n2023-12-15 11:03:21,737\tINFO util.py:129 -- Outdated packages:\n  ipywidgets==7.7.1 found, needs ipywidgets>=8\nRun `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n2023-12-15 11:03:22,653\tINFO util.py:129 -- Outdated packages:\n  ipywidgets==7.7.1 found, needs ipywidgets>=8\nRun `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n/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":"\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    755212  ultralytics.nn.modules.head.Detect           [20, [64, 128, 256]]          \nYOLOv8n summary: 225 layers, 3014748 parameters, 3014732 gradients, 8.2 GFLOPs\n\nTransferred 355/355 items from pretrained weights\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/Yolov8_1', view at http://localhost:6006/\n","output_type":"stream"},{"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"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.16.1"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20231215_110416-wh2z3vxy</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/8dmasters/YOLOv8/runs/wh2z3vxy' target=\"_blank\">Yolov8_1</a></strong> to <a href='https://wandb.ai/8dmasters/YOLOv8' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/8dmasters/YOLOv8' target=\"_blank\">https://wandb.ai/8dmasters/YOLOv8</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/8dmasters/YOLOv8/runs/wh2z3vxy' target=\"_blank\">https://wandb.ai/8dmasters/YOLOv8/runs/wh2z3vxy</a>"},"metadata":{}},{"name":"stdout","text":"Freezing 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/temp/Model_Dataset/train/labels... 19004 images, 0 backgrounds, 0 corrupt: 100%|██████████| 19004/19004 [00:23<00:00, 818.70it/s]","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /kaggle/temp/Model_Dataset/train/images/00594.jpg: 1 duplicate labels removed\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/temp/Model_Dataset/train/labels.cache\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/temp/Model_Dataset/val/labels... 2112 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2112/2112 [00:02<00:00, 792.80it/s]\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/temp/Model_Dataset/val/labels.cache\nPlotting labels to runs/detect/Yolov8_1/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=0.000417, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\nImage sizes 800 train, 800 val\nUsing 4 dataloader workers\nLogging results to \u001b[1mruns/detect/Yolov8_1\u001b[0m\nStarting training for 20 epochs...\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       1/20        13G      1.118     0.7517      1.118        152        800: 100%|██████████| 1188/1188 [06:18<00:00,  3.13it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:24<00:00,  2.70it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.822      0.749      0.801      0.554\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       2/20      10.7G      1.137     0.7658      1.128         92        800: 100%|██████████| 1188/1188 [06:11<00:00,  3.20it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.81it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.809      0.732      0.791      0.542\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       3/20      10.9G      1.146     0.7877      1.139        112        800: 100%|██████████| 1188/1188 [06:08<00:00,  3.22it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.76it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.824      0.732      0.789      0.538\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       4/20      10.6G      1.151     0.7901      1.137        138        800: 100%|██████████| 1188/1188 [06:16<00:00,  3.16it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.76it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.829      0.734      0.792       0.54\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       5/20      11.4G       1.15     0.7848      1.137        130        800: 100%|██████████| 1188/1188 [06:07<00:00,  3.23it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.83it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972       0.82      0.733      0.793      0.542\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       6/20      12.3G      1.143     0.7797      1.137        242        800: 100%|██████████| 1188/1188 [06:05<00:00,  3.25it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.92it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.819      0.732      0.795      0.546\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       7/20      9.41G      1.131     0.7729      1.131         81        800: 100%|██████████| 1188/1188 [06:02<00:00,  3.28it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.89it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972       0.84      0.737      0.798      0.549\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       8/20      12.9G      1.133     0.7623      1.127        565        800: 100%|██████████| 1188/1188 [06:03<00:00,  3.27it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.86it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.841      0.752      0.805      0.554\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"       9/20      12.4G      1.126     0.7554      1.126        133        800: 100%|██████████| 1188/1188 [06:03<00:00,  3.27it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.81it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.832      0.746      0.803      0.557\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      10/20      13.6G      1.125     0.7539      1.123        100        800: 100%|██████████| 1188/1188 [06:06<00:00,  3.24it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.87it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.812      0.758      0.804      0.555\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\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      11/20      13.4G      1.084      0.683      1.119         58        800: 100%|██████████| 1188/1188 [05:39<00:00,  3.50it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.91it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.855      0.745      0.811      0.564\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      12/20        13G       1.08     0.6687      1.115        108        800: 100%|██████████| 1188/1188 [05:36<00:00,  3.53it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.90it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.848      0.754      0.814      0.568\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      13/20      12.1G      1.072     0.6608      1.111         64        800: 100%|██████████| 1188/1188 [05:35<00:00,  3.54it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.81it/s]","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.849      0.768       0.82      0.575\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      14/20      10.3G      1.068     0.6545      1.108         87        800: 100%|██████████| 1188/1188 [05:41<00:00,  3.48it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.99it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.862      0.752      0.819      0.572\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      15/20      12.7G      1.061     0.6492      1.105        121        800: 100%|██████████| 1188/1188 [05:34<00:00,  3.55it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:23<00:00,  2.84it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972       0.86      0.754      0.821       0.58\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      16/20        11G      1.057     0.6404      1.102         48        800: 100%|██████████| 1188/1188 [05:34<00:00,  3.55it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.96it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.857      0.761      0.825      0.583\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      17/20      11.5G      1.051     0.6333      1.094         50        800: 100%|██████████| 1188/1188 [05:35<00:00,  3.54it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.94it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972       0.85      0.764      0.824      0.582\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      18/20      12.5G      1.046     0.6296      1.096         39        800: 100%|██████████| 1188/1188 [05:37<00:00,  3.52it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.88it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.854       0.77      0.825      0.584\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      19/20      8.69G      1.042     0.6257      1.091        409        800: 100%|██████████| 1188/1188 [05:35<00:00,  3.54it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.96it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.858      0.768      0.826      0.589\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      20/20      12.9G      1.036      0.616      1.087         46        800: 100%|██████████| 1188/1188 [05:37<00:00,  3.52it/s]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:22<00:00,  2.92it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.868      0.769      0.831      0.591\n\n20 epochs completed in 2.096 hours.\nOptimizer stripped from runs/detect/Yolov8_1/weights/last.pt, 6.3MB\nOptimizer stripped from runs/detect/Yolov8_1/weights/best.pt, 6.3MB\n\nValidating runs/detect/Yolov8_1/weights/best.pt...\nUltralytics YOLOv8.0.227 🚀 Python-3.10.12 torch-2.0.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\nYOLOv8n summary (fused): 168 layers, 3009548 parameters, 0 gradients, 8.1 GFLOPs\n","output_type":"stream"},{"name":"stderr","text":"                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 66/66 [00:37<00:00,  1.78it/s]\n","output_type":"stream"},{"name":"stdout","text":"                   all       2112      17972      0.867      0.769      0.831      0.591\n          Storage Tank       2112       2824      0.915      0.746       0.84      0.525\n        Baseball field       2112        449      0.947      0.949      0.974      0.834\n          Tennis court       2112       1085      0.959      0.933      0.965      0.835\n      Basketball Court       2112        294      0.929      0.874      0.927        0.8\n             Wind mill       2112        500      0.928      0.836      0.908      0.475\n               Vehicle       2112       4017       0.87      0.492      0.657      0.364\n                Harbor       2112        421      0.747      0.646      0.711      0.503\n                  Ship       2112       5556      0.931      0.927      0.954      0.589\n              Airplane       2112        912       0.96       0.94      0.969      0.704\n                Bridge       2112        376       0.85      0.428      0.565      0.311\n              Overpass       2112        251      0.879      0.635      0.744      0.505\nExpressway toll station       2112        125      0.897      0.656       0.78      0.589\n         Train station       2112        102      0.696      0.618      0.667      0.376\n               Chimney       2112        156       0.93      0.763      0.815      0.689\n    Ground Track Field       2112        289       0.82      0.841      0.889      0.705\n                   Dam       2112         87      0.708      0.668      0.715      0.399\nExpressway service area       2112        224       0.94      0.862      0.916      0.662\n               Stadium       2112        107      0.879      0.888      0.942      0.785\n               Airport       2112        105      0.779      0.838      0.835      0.574\n           Golf course       2112         92      0.784      0.837      0.847      0.589\nSpeed: 0.6ms preprocess, 3.0ms inference, 0.0ms loss, 1.2ms postprocess per image\nResults saved to \u001b[1mruns/detect/Yolov8_1\u001b[0m\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='18.374 MB of 18.374 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":""}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>lr/pg0</td><td>▃▆███▇▇▆▆▅▅▅▄▄▃▃▂▂▁▁</td></tr><tr><td>lr/pg1</td><td>▃▆███▇▇▆▆▅▅▅▄▄▃▃▂▂▁▁</td></tr><tr><td>lr/pg2</td><td>▃▆███▇▇▆▆▅▅▅▄▄▃▃▂▂▁▁</td></tr><tr><td>metrics/mAP50(B)</td><td>▃▁▁▂▂▂▃▄▃▃▅▅▆▆▆▇▇▇▇█</td></tr><tr><td>metrics/mAP50-95(B)</td><td>▃▂▁▁▂▂▂▃▄▃▄▅▆▆▇▇▇▇██</td></tr><tr><td>metrics/precision(B)</td><td>▃▁▃▃▂▂▅▅▄▁▇▆▆▇▇▇▆▆▇█</td></tr><tr><td>metrics/recall(B)</td><td>▄▁▁▁▁▁▂▅▄▆▃▅█▅▅▆▇███</td></tr><tr><td>model/GFLOPs</td><td>▁</td></tr><tr><td>model/parameters</td><td>▁</td></tr><tr><td>model/speed_PyTorch(ms)</td><td>▁</td></tr><tr><td>train/box_loss</td><td>▆▇████▇▇▆▆▄▄▃▃▃▂▂▂▁▁</td></tr><tr><td>train/cls_loss</td><td>▆▇████▇▇▇▇▄▃▃▃▂▂▂▂▁▁</td></tr><tr><td>train/dfl_loss</td><td>▅▇████▇▆▆▆▅▅▄▄▃▃▂▂▁▁</td></tr><tr><td>val/box_loss</td><td>▃▇▆▆█▅▇▆▄▃▃▃▂▃▂▂▃▂▁▁</td></tr><tr><td>val/cls_loss</td><td>▅▇█▇▇▇▆▆▅▅▄▄▃▃▂▂▂▂▁▁</td></tr><tr><td>val/dfl_loss</td><td>▄▇▇▇█▆▇▇▅▄▄▄▃▃▂▂▃▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>lr/pg0</td><td>5e-05</td></tr><tr><td>lr/pg1</td><td>5e-05</td></tr><tr><td>lr/pg2</td><td>5e-05</td></tr><tr><td>metrics/mAP50(B)</td><td>0.83096</td></tr><tr><td>metrics/mAP50-95(B)</td><td>0.5906</td></tr><tr><td>metrics/precision(B)</td><td>0.86734</td></tr><tr><td>metrics/recall(B)</td><td>0.76874</td></tr><tr><td>model/GFLOPs</td><td>8.215</td></tr><tr><td>model/parameters</td><td>3014748</td></tr><tr><td>model/speed_PyTorch(ms)</td><td>2.85</td></tr><tr><td>train/box_loss</td><td>1.03606</td></tr><tr><td>train/cls_loss</td><td>0.61603</td></tr><tr><td>train/dfl_loss</td><td>1.08733</td></tr><tr><td>val/box_loss</td><td>1.07601</td></tr><tr><td>val/cls_loss</td><td>0.60958</td></tr><tr><td>val/dfl_loss</td><td>1.08251</td></tr></table><br/></div></div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run <strong style=\"color:#cdcd00\">Yolov8_1</strong> at: <a href='https://wandb.ai/8dmasters/YOLOv8/runs/wh2z3vxy' target=\"_blank\">https://wandb.ai/8dmasters/YOLOv8/runs/wh2z3vxy</a><br/>Synced 6 W&B file(s), 24 media file(s), 5 artifact file(s) and 0 other file(s)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Find logs at: <code>./wandb/run-20231215_110416-wh2z3vxy/logs</code>"},"metadata":{}},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"ultralytics.utils.metrics.DetMetrics object with attributes:\n\nap_class_index: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])\nbox: ultralytics.utils.metrics.Metric object\nconfusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7862c1c62a10>\ncurves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\ncurves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,   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0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.20439,     0.20439,     0.24672, ...,           1,           1,           1],\n       [    0.13019,     0.13019,     0.19462, ...,           1,           1,           1],\n       [    0.17503,     0.17503,     0.23849, ...,           1,           1,           1],\n       ...,\n       [    0.13125,     0.13125,     0.18647, ...,           1,           1,           1],\n       [   0.071379,    0.071379,     0.10209, ...,           1,           1,           1],\n       [   0.047771,    0.047771,    0.077422, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    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0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[     0.8665,      0.8665,     0.86579, ...,           0,           0,           0],\n       [    0.98441,     0.98441,     0.98441, ...,           0,           0,           0],\n       [    0.97419,     0.97419,     0.97419, ...,           0,           0,           0],\n       ...,\n       [    0.98131,     0.98131,     0.98131, ...,           0,           0,           0],\n       [    0.98095,     0.98095,     0.97143, ...,           0,           0,           0],\n       [    0.97826,     0.97826,     0.96739, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\nfitness: 0.6146361117769853\nkeys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\nmaps: array([    0.52467,     0.83441,      0.8347,      0.7998,     0.47515,     0.36414,     0.50343,     0.58939,     0.70357,     0.31069,     0.50531,       0.589,     0.37564,      0.6886,     0.70502,     0.39892,     0.66184,     0.78514,     0.57389,     0.58867])\nnames: {0: 'Storage Tank', 1: 'Baseball field', 10: 'Overpass', 11: 'Expressway toll station', 12: 'Train station', 13: 'Chimney', 14: 'Ground Track Field', 15: 'Dam', 16: 'Expressway service area', 17: 'Stadium', 18: 'Airport', 19: 'Golf course', 2: 'Tennis court', 3: 'Basketball Court', 4: 'Wind mill', 5: 'Vehicle', 6: 'Harbor', 7: 'Ship', 8: 'Airplane', 9: 'Bridge'}\nplot: True\nresults_dict: {'metrics/precision(B)': 0.8673394793067988, 'metrics/recall(B)': 0.7687391965416251, 'metrics/mAP50(B)': 0.8309617533325971, 'metrics/mAP50-95(B)': 0.5905999293819173, 'fitness': 0.6146361117769853}\nsave_dir: PosixPath('runs/detect/Yolov8_1')\nspeed: {'preprocess': 0.5559546477866896, 'inference': 2.9701661657203324, 'loss': 0.0008401094060955625, 'postprocess': 1.2374031498576656}\ntask: 'detect'"},"metadata":{}}]}]}