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{"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"]}]}]} |