{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyOCqa7FkdGyF/CF5ru7IEXm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["# This mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","%cd /content/drive/My\\ Drive/My\\ Projects/yolo_custom_training"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fl0fMqnr1O1V","executionInfo":{"status":"ok","timestamp":1689616601309,"user_tz":240,"elapsed":23958,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"2add0d4d-0982-47f9-8bbd-a0ead1a50a06"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/My Drive/My Projects/yolo_custom_training\n"]}]},{"cell_type":"code","source":["import os\n","HOME = os.getcwd()\n","print(HOME)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5lwQa3PijESC","executionInfo":{"status":"ok","timestamp":1689616604881,"user_tz":240,"elapsed":136,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"964cd55b-1f1e-44f9-c853-d6d9fcee16f0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/My Drive/My Projects/yolo_custom_training\n"]}]},{"cell_type":"markdown","source":["## Install YOLOv8"],"metadata":{"id":"I6NSNz9zbTDA"}},{"cell_type":"code","source":["# Pip install method (recommended)\n","!pip install ultralytics==8.0.20\n","\n","from IPython import display\n","display.clear_output()\n","\n","import ultralytics\n","ultralytics.checks()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NO5AyAFMbH1I","executionInfo":{"status":"ok","timestamp":1689616619862,"user_tz":240,"elapsed":13292,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"81894275-1307-4f83-90ce-8cc31b480d4e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Ultralytics YOLOv8.0.20 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.3/78.2 GB disk)\n"]}]},{"cell_type":"markdown","source":["### Loading Custom Dataset"],"metadata":{"id":"ntvmUKx7ty-k"}},{"cell_type":"markdown","source":["1. From RoboFlow"],"metadata":{"id":"nQUe0Mect5yu"}},{"cell_type":"code","source":["!pip install roboflow --quiet\n","\n","from roboflow import Roboflow\n","rf = Roboflow(api_key=\"dNG5RONSeTdSdNyKbktT\")\n","project = rf.workspace(\"vaishanth-ramaraj-uub2b\").project(\"hand-detector-pjtzx\")\n","dataset = project.version(3).download(\"yolov8\")"],"metadata":{"id":"uAGS2ETut9Q8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["2. Locally"],"metadata":{"id":"e2Nt70vLuHKv"}},{"cell_type":"code","source":["dataset_type = \"hand_dataset_seg_v2\" # [\"hand_dataset_seg_v1\", \"hand_dataset_seg_v2\"]\n","\n","dataset_path_abs = \"/content/drive/My\\ Drive/My\\ Projects/yolo_custom_training/datasets/\"+dataset_type+\"/data.yaml\"\n","dataset_path_rel = \"datasets/\"+dataset_type+\"/data.yaml\"\n","\n","print(\"Absolute path: \", dataset_path_abs)\n","print(\"Relative path: \", dataset_path_rel)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s7cfNjBbuMiQ","executionInfo":{"status":"ok","timestamp":1689616623473,"user_tz":240,"elapsed":99,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"52efe37e-f1c8-4a65-a3e8-6ed88ef250db"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Absolute path:  /content/drive/My\\ Drive/My\\ Projects/yolo_custom_training/datasets/hand_dataset_seg_v2/data.yaml\n","Relative path:  datasets/hand_dataset_seg_v2/data.yaml\n"]}]},{"cell_type":"markdown","source":["### Training Custom Model"],"metadata":{"id":"N90ZJOD8vXjF"}},{"cell_type":"code","source":["from ultralytics import YOLO\n","\n","# Load the model.\n","model = YOLO('yolov8m-seg.pt')\n","\n","CUSTOM_MODEL_NAME = \"yolo8m_seg_hand\"\n","\n","# Training with custom dataset\n","results = model.train(\n","   data=dataset_path_rel,\n","   imgsz=640,\n","   epochs=50,\n","   batch=8,\n","   name=CUSTOM_MODEL_NAME)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"60QljZEJvarc","executionInfo":{"status":"ok","timestamp":1689618405675,"user_tz":240,"elapsed":844714,"user":{"displayName":"Vaishanth Ramaraj","userId":"09348595743611064915"}},"outputId":"1dd83ca3-c94a-4a38-e215-c164fca7b48d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt to yolov8m-seg.pt...\n","100%|██████████| 52.4M/52.4M [00:00<00:00, 95.8MB/s]\n","\n","Ultralytics YOLOv8.0.20 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8m-seg.yaml, data=datasets/hand_dataset_seg_v2/data.yaml, epochs=50, patience=50, batch=8, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=yolo8m_seg_hand, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, 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=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.1, copy_paste=0.1, cfg=None, v5loader=False, save_dir=runs/segment/yolo8m_seg_hand\n","Overriding model.yaml nc=80 with nc=2\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1      1392  ultralytics.nn.modules.Conv                  [3, 48, 3, 2]                 \n","  1                  -1  1     41664  ultralytics.nn.modules.Conv                  [48, 96, 3, 2]                \n","  2                  -1  2    111360  ultralytics.nn.modules.C2f                   [96, 96, 2, True]             \n","  3                  -1  1    166272  ultralytics.nn.modules.Conv                  [96, 192, 3, 2]               \n","  4                  -1  4    813312  ultralytics.nn.modules.C2f                   [192, 192, 4, True]           \n","  5                  -1  1    664320  ultralytics.nn.modules.Conv                  [192, 384, 3, 2]              \n","  6                  -1  4   3248640  ultralytics.nn.modules.C2f                   [384, 384, 4, True]           \n","  7                  -1  1   1991808  ultralytics.nn.modules.Conv                  [384, 576, 3, 2]              \n","  8                  -1  2   3985920  ultralytics.nn.modules.C2f                   [576, 576, 2, True]           \n","  9                  -1  1    831168  ultralytics.nn.modules.SPPF                  [576, 576, 5]                 \n"," 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n"," 12                  -1  2   1993728  ultralytics.nn.modules.C2f                   [960, 384, 2]                 \n"," 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n"," 15                  -1  2    517632  ultralytics.nn.modules.C2f                   [576, 192, 2]                 \n"," 16                  -1  1    332160  ultralytics.nn.modules.Conv                  [192, 192, 3, 2]              \n"," 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n"," 18                  -1  2   1846272  ultralytics.nn.modules.C2f                   [576, 384, 2]                 \n"," 19                  -1  1   1327872  ultralytics.nn.modules.Conv                  [384, 384, 3, 2]              \n"," 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n"," 21                  -1  2   4207104  ultralytics.nn.modules.C2f                   [960, 576, 2]                 \n"," 22        [15, 18, 21]  1   5160182  ultralytics.nn.modules.Segment               [2, 32, 192, [192, 384, 576]] \n","YOLOv8m-seg summary: 331 layers, 27240806 parameters, 27240790 gradients, 110.4 GFLOPs\n","\n","Transferred 531/537 items from pretrained weights\n","\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 86 weight(decay=0.0), 97 weight(decay=0.001), 96 bias\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/My Drive/My Projects/yolo_custom_training/datasets/hand_dataset_seg_v2/train/labels.cache... 165 images, 0 backgrounds, 0 corrupt: 100%|██████████| 165/165 [00:00<?, ?it/s]\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","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/My Drive/My Projects/yolo_custom_training/datasets/hand_dataset_seg_v2/valid/labels.cache... 10 images, 0 backgrounds, 0 corrupt: 100%|██████████| 10/10 [00:00<?, ?it/s]\n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1mruns/segment/yolo8m_seg_hand\u001b[0m\n","Starting training for 50 epochs...\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       1/50      4.65G      1.794       3.48      3.851      1.643         25        640: 100%|██████████| 21/21 [00:17<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.37it/s]\n","                   all         10         13      0.204      0.488      0.347       0.27      0.204      0.488      0.351      0.261\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       2/50      4.88G      1.488      2.528      2.393      1.389         14        640: 100%|██████████| 21/21 [00:15<00:00,  1.39it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n","                   all         10         13      0.521      0.894      0.656      0.486      0.521      0.894      0.656      0.498\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       3/50      4.88G      1.399      2.342       2.17      1.301         16        640: 100%|██████████| 21/21 [00:15<00:00,  1.36it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.51it/s]\n","                   all         10         13      0.485      0.833      0.559      0.474      0.485      0.833      0.559      0.447\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       4/50      4.88G      1.399      2.325      2.225      1.264         15        640: 100%|██████████| 21/21 [00:14<00:00,  1.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n","                   all         10         13      0.627      0.664      0.713      0.552      0.679      0.735      0.726      0.534\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       5/50      4.88G      1.382      2.294      2.365      1.178         18        640: 100%|██████████| 21/21 [00:14<00:00,  1.41it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.52it/s]\n","                   all         10         13          1      0.544      0.833      0.681          1      0.544      0.833      0.672\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       6/50      4.88G      1.336      2.233      2.021      1.219         23        640: 100%|██████████| 21/21 [00:14<00:00,  1.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.45it/s]\n","                   all         10         13      0.707      0.762      0.758       0.67      0.707      0.762      0.758      0.616\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       7/50      4.88G      1.343      2.271      1.867      1.178         10        640: 100%|██████████| 21/21 [00:15<00:00,  1.39it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.61it/s]\n","                   all         10         13      0.784      0.825       0.87      0.738      0.784      0.825       0.87      0.717\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       8/50      4.88G      1.325      2.085      1.505      1.116         12        640: 100%|██████████| 21/21 [00:15<00:00,  1.39it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.18it/s]\n","                   all         10         13      0.839      0.619      0.835      0.664      0.839      0.619      0.835      0.679\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       9/50      4.88G      1.432      2.267      1.499      1.202         22        640: 100%|██████████| 21/21 [00:14<00:00,  1.40it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.63it/s]\n","                   all         10         13      0.751      0.786      0.852      0.679      0.751      0.786      0.852      0.645\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      10/50      4.88G      1.387      2.133      1.418      1.183         31        640: 100%|██████████| 21/21 [00:14<00:00,  1.41it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.61it/s]\n","                   all         10         13      0.829      0.741      0.777      0.677      0.829      0.741      0.777      0.617\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      11/50      4.88G      1.439      2.146       1.43      1.243         15        640: 100%|██████████| 21/21 [00:14<00:00,  1.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.61it/s]\n","                   all         10         13      0.703      0.845      0.878      0.703      0.703      0.845      0.878      0.687\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      12/50      4.88G      1.356      2.115      1.373      1.208          5        640: 100%|██████████| 21/21 [00:14<00:00,  1.48it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.61it/s]\n","                   all         10         13      0.664      0.917      0.832      0.695      0.664      0.917      0.832      0.672\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      13/50      4.88G      1.342      2.207      1.301      1.155         14        640: 100%|██████████| 21/21 [00:14<00:00,  1.41it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n","                   all         10         13      0.704      0.917      0.862       0.73      0.704      0.917      0.862      0.719\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      14/50      4.88G      1.341       2.14      1.301      1.138         22        640: 100%|██████████| 21/21 [00:14<00:00,  1.47it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.21it/s]\n","                   all         10         13      0.673      0.917      0.841      0.651      0.673      0.917      0.841      0.669\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      15/50      4.88G      1.394      2.267      1.466      1.197         19        640: 100%|██████████| 21/21 [00:14<00:00,  1.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n","                   all         10         13      0.782      0.884      0.923        0.8      0.782      0.884      0.923      0.755\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      16/50      4.88G      1.299      2.073      1.293      1.107         15        640: 100%|██████████| 21/21 [00:15<00:00,  1.33it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.62it/s]\n","                   all         10         13      0.853      0.836      0.852      0.702      0.853      0.836      0.852        0.7\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      17/50      4.88G       1.28      2.154      1.166      1.117         33        640: 100%|██████████| 21/21 [00:14<00:00,  1.47it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.50it/s]\n","                   all         10         13      0.848      0.857      0.806       0.64      0.848      0.857      0.806      0.634\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      18/50      4.88G        1.3       2.04      1.208      1.153         15        640: 100%|██████████| 21/21 [00:14<00:00,  1.47it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.18it/s]\n","                   all         10         13       0.75      0.914      0.874      0.742       0.75      0.914      0.874      0.698\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      19/50      4.88G      1.314      2.032      1.163      1.143         16        640: 100%|██████████| 21/21 [00:14<00:00,  1.45it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n","                   all         10         13      0.629      0.918      0.864       0.74      0.629      0.918      0.864      0.674\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      20/50      4.88G      1.346      2.148      1.146      1.165         25        640: 100%|██████████| 21/21 [00:15<00:00,  1.40it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.63it/s]\n","                   all         10         13      0.762      0.845      0.866      0.733      0.762      0.845      0.866      0.727\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      21/50      4.88G      1.321       1.97       1.14      1.133         18        640: 100%|██████████| 21/21 [00:14<00:00,  1.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.65it/s]\n","                   all         10         13      0.773      0.845      0.827      0.704      0.773      0.845      0.827      0.659\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      22/50      4.88G      1.316      1.924       1.16      1.137         29        640: 100%|██████████| 21/21 [00:14<00:00,  1.45it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n","                   all         10         13      0.884      0.678      0.841      0.735      0.884      0.678      0.841      0.688\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      23/50      4.88G      1.263      2.002      1.092      1.112         18        640: 100%|██████████| 21/21 [00:14<00:00,  1.44it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n","                   all         10         13       0.71       0.77      0.793      0.681       0.71       0.77      0.793      0.667\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      24/50      4.88G      1.314       2.06      1.122      1.132         14        640: 100%|██████████| 21/21 [00:14<00:00,  1.48it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.20it/s]\n","                   all         10         13      0.874      0.782      0.921      0.793      0.874      0.782      0.921      0.751\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      25/50      4.88G      1.363      2.284      1.102      1.175         30        640: 100%|██████████| 21/21 [00:14<00:00,  1.48it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.19it/s]\n","                   all         10         13      0.796      0.916      0.936      0.771      0.796      0.916      0.936      0.738\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      26/50      4.88G      1.233      2.017     0.9702      1.107         10        640: 100%|██████████| 21/21 [00:14<00:00,  1.47it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n","                   all         10         13      0.875      0.929      0.954      0.825      0.875      0.929      0.954      0.774\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      27/50      4.88G      1.207      1.929     0.9747       1.09         19        640: 100%|██████████| 21/21 [00:14<00:00,  1.41it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.63it/s]\n","                   all         10         13      0.918      0.929      0.963       0.82      0.918      0.929      0.963      0.779\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      28/50      4.88G       1.26      1.878      1.014      1.151         31        640: 100%|██████████| 21/21 [00:15<00:00,  1.40it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.63it/s]\n","                   all         10         13      0.904      0.929      0.942      0.774      0.904      0.929      0.942      0.781\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      29/50      4.88G      1.201      1.957     0.8977      1.106         22        640: 100%|██████████| 21/21 [00:14<00:00,  1.45it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.22it/s]\n","                   all         10         13      0.953      0.929      0.958      0.804      0.953      0.929      0.958      0.782\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      30/50      4.88G       1.24      1.939     0.9517       1.11         11        640: 100%|██████████| 21/21 [00:14<00:00,  1.44it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]\n","                   all         10         13      0.755       0.91      0.932      0.735      0.755       0.91      0.932      0.771\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      31/50      4.88G      1.223      1.887     0.9285      1.096         25        640: 100%|██████████| 21/21 [00:14<00:00,  1.43it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n","                   all         10         13      0.787       0.84      0.902      0.713      0.787       0.84      0.902      0.721\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      32/50      4.88G      1.143      1.747     0.8498      1.075         19        640: 100%|██████████| 21/21 [00:14<00:00,  1.43it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.63it/s]\n","                   all         10         13      0.798      0.915      0.932       0.81      0.798      0.915      0.932       0.78\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      33/50      4.88G      1.113      1.844     0.8374      1.064         14        640: 100%|██████████| 21/21 [00:14<00:00,  1.47it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.62it/s]\n","                   all         10         13      0.774      0.837      0.845      0.672      0.774      0.837      0.845      0.658\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      34/50      4.88G      1.194      1.973     0.8466      1.082         16        640: 100%|██████████| 21/21 [00:14<00:00,  1.46it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.61it/s]\n","                   all         10         13      0.797      0.767      0.806       0.67      0.797      0.767      0.806      0.652\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      35/50      4.88G        1.2      1.944     0.9042      1.085         26        640: 100%|██████████| 21/21 [00:14<00:00,  1.45it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.21it/s]\n","                   all         10         13      0.843      0.774      0.802      0.704      0.843      0.774      0.802      0.659\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      36/50      4.88G      1.136      1.732     0.7948      1.056         15        640: 100%|██████████| 21/21 [00:14<00:00,  1.48it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.56it/s]\n","                   all         10         13      0.901      0.845      0.954      0.837      0.901      0.845      0.954      0.805\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      37/50      4.88G       1.11      1.813     0.8218      1.052         21        640: 100%|██████████| 21/21 [00:15<00:00,  1.35it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n","                   all         10         13      0.914      0.921      0.956      0.863      0.914      0.921      0.956      0.814\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      38/50      4.88G      1.053      1.663     0.8094      1.007         17        640: 100%|██████████| 21/21 [00:14<00:00,  1.46it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.15it/s]\n","                   all         10         13      0.904      0.929      0.968      0.839      0.904      0.929      0.968      0.824\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      39/50      4.88G      1.164      1.897     0.7986      1.078         27        640: 100%|██████████| 21/21 [00:14<00:00,  1.48it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n","                   all         10         13      0.853      0.929      0.926      0.826      0.853      0.929      0.926      0.783\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      40/50      4.88G      1.188      1.882     0.8029      1.073         22        640: 100%|██████████| 21/21 [00:14<00:00,  1.41it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n","                   all         10         13      0.819      0.919      0.926      0.798      0.819      0.919      0.926      0.779\n","Closing 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   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      41/50      4.88G      1.048       1.88     0.6224      1.119          8        640: 100%|██████████| 21/21 [00:10<00:00,  1.95it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.62it/s]\n","                   all         10         13      0.856      0.834      0.888      0.762      0.856      0.834      0.888       0.75\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      42/50      4.88G      1.038       1.85     0.5709       1.12          9        640: 100%|██████████| 21/21 [00:07<00:00,  2.70it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]\n","                   all         10         13      0.836      0.845      0.876      0.726      0.836      0.845      0.876       0.75\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      43/50      4.88G       1.05      1.927     0.5746      1.125         10        640: 100%|██████████| 21/21 [00:07<00:00,  2.67it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.38it/s]\n","                   all         10         13      0.786      0.837      0.828      0.706      0.786      0.837      0.828      0.704\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      44/50      4.88G      1.038      1.882     0.5697      1.127          8        640: 100%|██████████| 21/21 [00:09<00:00,  2.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n","                   all         10         13      0.782      0.845      0.855      0.757      0.782      0.845      0.855      0.739\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      45/50      4.88G     0.9828      1.804      0.555      1.077         10        640: 100%|██████████| 21/21 [00:09<00:00,  2.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.53it/s]\n","                   all         10         13      0.839       0.77      0.852      0.738      0.839       0.77      0.852      0.738\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      46/50      4.88G      1.024      1.885     0.5223      1.096          8        640: 100%|██████████| 21/21 [00:08<00:00,  2.53it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.11it/s]\n","                   all         10         13      0.834      0.741      0.875       0.78      0.834      0.741      0.875      0.754\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      47/50      4.88G     0.9782      1.789     0.5043      1.102          7        640: 100%|██████████| 21/21 [00:07<00:00,  2.69it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.33it/s]\n","                   all         10         13      0.879      0.774      0.866      0.778      0.879      0.774      0.866      0.745\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      48/50      4.88G     0.9752      1.831     0.4972       1.06          8        640: 100%|██████████| 21/21 [00:09<00:00,  2.30it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.65it/s]\n","                   all         10         13      0.846      0.774      0.866       0.77      0.846      0.774      0.866      0.741\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      49/50      4.88G     0.9708      1.761     0.5103      1.074          8        640: 100%|██████████| 21/21 [00:08<00:00,  2.33it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.52it/s]\n","                   all         10         13      0.886      0.774      0.866      0.768      0.886      0.774      0.866      0.745\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      50/50      4.88G       1.02      1.811     0.5085      1.109          7        640: 100%|██████████| 21/21 [00:08<00:00,  2.60it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.27s/it]\n","                   all         10         13      0.904       0.77      0.866      0.773      0.904       0.77      0.866      0.744\n","\n","50 epochs completed in 0.230 hours.\n","Optimizer stripped from runs/segment/yolo8m_seg_hand/weights/last.pt, 54.8MB\n","Optimizer stripped from runs/segment/yolo8m_seg_hand/weights/best.pt, 54.8MB\n","\n","Validating runs/segment/yolo8m_seg_hand/weights/best.pt...\n","Ultralytics YOLOv8.0.20 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n","YOLOv8m-seg summary (fused): 245 layers, 27223542 parameters, 0 gradients, 110.0 GFLOPs\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.21it/s]\n","                   all         10         13      0.914      0.921      0.956      0.863      0.914      0.921      0.956      0.816\n","             left_hand         10          6      0.855      0.985      0.948      0.877      0.855      0.985      0.948      0.833\n","            right_hand         10          7      0.973      0.857      0.964       0.85      0.973      0.857      0.964      0.799\n","Speed: 0.3ms pre-process, 16.6ms inference, 0.0ms loss, 1.1ms post-process per image\n","Results saved to \u001b[1mruns/segment/yolo8m_seg_hand\u001b[0m\n"]}]}]}