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+{"cells":[{"cell_type":"markdown","metadata":{"id":"CK35QGu-QFzl"},"source":["#**Train YOLOv8 for Custom Instance Segmentation**\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":437,"status":"ok","timestamp":1708524198792,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"o1FGOqCyP7Eq","outputId":"05290071-9fd5-4481-dcce-5054032e7dd9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Wed Feb 21 14:03:18 2024       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n","| N/A   51C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","|  No running processes found                                                           |\n","+---------------------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"markdown","metadata":{"id":"l8rXJHI8QYgq"},"source":["##Install YOLOv8\n"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1708524671826,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"w8-YovslJ8sh","outputId":"97a4d65f-8358-486a-e8fe-ccc176ba6e71"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/yolov8\n"]}],"source":["%cd /content/drive/MyDrive/yolov8"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5672,"status":"ok","timestamp":1708524683182,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"bldGBzeyQa-l","outputId":"b6c53351-1e2a-4d3b-a545-7e1398684961"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: ultralytics in /usr/local/lib/python3.10/dist-packages (8.1.17)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n","Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n","Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.1.0+cu121)\n","Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.16.0+cu121)\n","Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.2)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n","Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n","Requirement already satisfied: thop>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.1.1.post2209072238)\n","Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.5.3)\n","Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.10.0)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.49.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n","Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.25.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (23.2)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.1)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.4)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2023.7.22)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.13.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.9.0)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.2.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.3)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from cycler>=0.10->matplotlib>=3.3.0->ultralytics) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n"]}],"source":["!pip install ultralytics"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":405,"status":"ok","timestamp":1708524694681,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"onGqK71GQep6"},"outputs":[],"source":["import os\n","from IPython import display\n","display.clear_output()\n","#!yolo mode=checks\n","\n","from ultralytics import YOLO\n","\n","from IPython.display import display, Image"]},{"cell_type":"markdown","metadata":{"id":"5J_xJQ1qQ1Sm"},"source":["##Instance Segmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gwyE-4moQ4Tq"},"outputs":[],"source":["!yolo task=segment mode=predict model=yolov8l-seg.pt conf=0.4 source='/content/drive/MyDrive/ball2.png'"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":298},"executionInfo":{"elapsed":407,"status":"error","timestamp":1708414860924,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"hYZ0_dabRLM7","outputId":"3ec84937-e4de-43d8-c638-6be313de4cce"},"outputs":[{"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/content/runs/segment/predict4/ball2.png'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-5-eed8e146ea5a>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/content/runs/segment/predict4/ball2.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretina\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m         super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0m\u001b[1;32m   1232\u001b[0m                 metadata=metadata)\n\u001b[1;32m   1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    638\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1264\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    663\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/runs/segment/predict4/ball2.png'"]}],"source":["Image(filename='/content/runs/segment/predict4/ball2.png', height=600)"]},{"cell_type":"markdown","metadata":{"id":"3tPIyQGZRR0Q"},"source":["##Import Dataset from Roboflow"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6750,"status":"ok","timestamp":1708524711030,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"QTo6OKOYRffT","outputId":"0420ccf1-a733-4bdf-f18f-a6ce6f6c25de"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.19)\n","Requirement already satisfied: certifi==2023.7.22 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2023.7.22)\n","Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n","Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.10.0)\n","Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.10)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n","Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.25.2)\n","Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n","Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n","Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n","Requirement already satisfied: supervision in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.18.0)\n","Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.7)\n","Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.2)\n","Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n","Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n","Requirement already satisfied: python-magic in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.4.27)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.0)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.49.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (23.2)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.1.1)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)\n","Requirement already satisfied: defusedxml<0.8.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (0.7.1)\n","Requirement already satisfied: scipy<2.0.0,>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from supervision->roboflow) (1.11.4)\n","loading Roboflow workspace...\n","loading Roboflow project...\n","Dependency ultralytics==8.0.196 is required but found version=8.1.17, to fix: `pip install ultralytics==8.0.196`\n"]}],"source":["!pip install roboflow\n","from roboflow import Roboflow\n","rf = Roboflow(api_key=\"8Su4eKkFHCtjENDdpEik\")\n","project = rf.workspace(\"iitp-w6lkv\").project(\"2024-kozt7\")\n","dataset = project.version(4).download(\"yolov8\")"]},{"cell_type":"markdown","metadata":{"id":"WHZPT6GfR4Cm"},"source":["##Train the YOLOv8 Model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":446485,"status":"ok","timestamp":1708429103302,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"yLmhP85qR7hj","outputId":"ee121db5-9f22-46e3-8499-c5f216deb16f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=segment, mode=train, model=yolov8l-seg.pt, data=/content/drive/MyDrive/yolov8/Ball-Column-Segment-1/data.yaml, epochs=15, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train3\n","2024-02-20 11:31:01.001868: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-02-20 11:31:01.001922: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-02-20 11:31:01.003142: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","Overriding model.yaml nc=80 with nc=1\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n","  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n","  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n","  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n","  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n","  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n","  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n","  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n","  8                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           \n","  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n"," 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 12                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \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  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 \n"," 16                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n"," 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 18                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 \n"," 19                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n"," 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 21                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n"," 22        [15, 18, 21]  1   7889779  ultralytics.nn.modules.head.Segment          [1, 32, 256, [256, 512, 512]] \n","YOLOv8l-seg summary: 401 layers, 45936819 parameters, 45936803 gradients, 220.8 GFLOPs\n","\n","Transferred 651/657 items from pretrained weights\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train3', view at http://localhost:6006/\n","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","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/yolov8/Ball-Column-Segment-1/train/labels.cache... 267 images, 3 backgrounds, 0 corrupt: 100% 267/267 [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/MyDrive/yolov8/Ball-Column-Segment-1/valid/labels.cache... 25 images, 0 backgrounds, 0 corrupt: 100% 25/25 [00:00<?, ?it/s]\n","Plotting labels to runs/segment/train3/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=0.002, momentum=0.9) with parameter groups 106 weight(decay=0.0), 117 weight(decay=0.0005), 116 bias(decay=0.0)\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1mruns/segment/train3\u001b[0m\n","Starting training for 15 epochs...\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       1/15      11.6G     0.9108        1.6      2.662      1.002         63        640: 100% 17/17 [00:21<00:00,  1.26s/it]\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         25        111     0.0886      0.919     0.0966     0.0744      0.086      0.892     0.0919      0.066\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       2/15      12.1G      0.701      1.238     0.8732     0.9288         78        640: 100% 17/17 [00:17<00:00,  1.02s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.03it/s]\n","                   all         25        111     0.0329      0.784     0.0328     0.0228     0.0298      0.712     0.0275     0.0182\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       3/15      12.4G     0.7146       1.29     0.7398     0.9379         97        640: 100% 17/17 [00:17<00:00,  1.05s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.31it/s]\n","                   all         25        111    0.00656      0.387    0.00622    0.00521    0.00608       0.36    0.00541    0.00396\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       4/15      11.9G     0.7251      1.313     0.8579      0.946         81        640: 100% 17/17 [00:17<00:00,  1.04s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.25it/s]\n","                   all         25        111     0.0016      0.108   0.000887   0.000681     0.0016      0.108   0.000887   0.000561\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       5/15      12.2G     0.7713      1.277     0.7245     0.9646         71        640: 100% 17/17 [00:18<00:00,  1.06s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.40it/s]\n","                   all         25        111    0.00287      0.153    0.00152   0.000882    0.00236      0.126    0.00119   0.000672\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","       6/15        12G     0.7412      1.254     0.6929     0.9566         54        640: 100% 17/17 [00:19<00:00,  1.13s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.25it/s]\n","                   all         25        111      0.704      0.812      0.777      0.624      0.704      0.812       0.77      0.581\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       7/15      11.9G     0.7182      1.277     0.6647     0.9642         63        640: 100% 17/17 [00:17<00:00,  1.01s/it]\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         25        111   0.000827     0.0541   0.000423   0.000236   0.000667      0.045   0.000348   0.000165\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       8/15        12G     0.7558       1.42     0.6933     0.9754         33        640: 100% 17/17 [00:17<00:00,  1.06s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.27it/s]\n","                   all         25        111   0.000154    0.00901   6.75e-05   2.02e-05   0.000154    0.00901   6.75e-05    2.7e-05\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","       9/15      12.5G     0.7096      1.163       0.66     0.9581         50        640: 100% 17/17 [00:17<00:00,  1.03s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.26it/s]\n","                   all         25        111      0.799      0.829      0.852      0.675      0.833      0.764      0.821      0.577\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      10/15      11.9G     0.6311      1.024     0.5718     0.9076         41        640: 100% 17/17 [00:18<00:00,  1.09s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:01<00:00,  1.09s/it]\n","                   all         25        111      0.719      0.877      0.835      0.698       0.81      0.739      0.818      0.641\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      11/15        12G     0.6066     0.9764     0.5256     0.9047         51        640: 100% 17/17 [00:18<00:00,  1.07s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.14it/s]\n","                   all         25        111      0.901      0.905      0.914      0.764       0.89      0.892      0.903      0.697\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      12/15        12G     0.6246     0.9648     0.5194     0.9257         52        640: 100% 17/17 [00:17<00:00,  1.06s/it]\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         25        111      0.885      0.883      0.943       0.78        0.9      0.856      0.915      0.707\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      13/15        12G     0.5585     0.8675      0.456     0.8863         56        640: 100% 17/17 [00:17<00:00,  1.04s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.36it/s]\n","                   all         25        111      0.924      0.892      0.951      0.818      0.942      0.856      0.936      0.756\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      14/15      11.9G     0.5288     0.8183     0.4168     0.8692         80        640: 100% 17/17 [00:18<00:00,  1.06s/it]\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         25        111      0.938      0.892      0.951      0.837      0.938      0.892      0.945      0.748\n","\n","      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n","      15/15      12.1G     0.5126     0.7789     0.3963     0.8602         52        640: 100% 17/17 [00:18<00:00,  1.07s/it]\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         25        111      0.915      0.901      0.954      0.848      0.915      0.901      0.947      0.769\n","\n","15 epochs completed in 0.113 hours.\n","Optimizer stripped from runs/segment/train3/weights/last.pt, 92.3MB\n","Optimizer stripped from runs/segment/train3/weights/best.pt, 92.3MB\n","\n","Validating runs/segment/train3/weights/best.pt...\n","Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","YOLOv8l-seg summary (fused): 295 layers, 45912659 parameters, 0 gradients, 220.1 GFLOPs\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  1.40it/s]\n","                   all         25        111      0.955      0.865      0.955      0.849      0.955      0.865      0.947      0.769\n","Speed: 0.2ms preprocess, 22.2ms inference, 0.0ms loss, 1.0ms postprocess per image\n","Results saved to \u001b[1mruns/segment/train3\u001b[0m\n","💡 Learn more at https://docs.ultralytics.com/modes/train\n"]}],"source":["!yolo task=segment mode=train model=yolov8l-seg.pt data=/content/drive/MyDrive/yolov8/Ball-Column-Segment-1/data.yaml epochs=15 imgsz=640"]},{"cell_type":"markdown","metadata":{"id":"-AREL_mCj8lB"},"source":["###Train task detect"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11096,"status":"ok","timestamp":1708428442643,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"q0jQMxt4j1Oq","outputId":"2a6fcd37-387c-4445-90f6-246b0f94c98a"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading Roboflow workspace...\n","loading Roboflow project...\n","Dependency ultralytics==8.0.196 is required but found version=8.1.16, to fix: `pip install ultralytics==8.0.196`\n"]},{"name":"stderr","output_type":"stream","text":["Downloading Dataset Version Zip in Robocon-6 to yolov8:: 100%|██████████| 18384/18384 [00:01<00:00, 10283.77it/s]"]},{"name":"stdout","output_type":"stream","text":["\n"]},{"name":"stderr","output_type":"stream","text":["\n","Extracting Dataset Version Zip to Robocon-6 in yolov8:: 100%|██████████| 978/978 [00:06<00:00, 145.55it/s]\n"]}],"source":["from roboflow import Roboflow\n","rf = Roboflow(api_key=\"8IAdBThHBpznBgBV5Ucm\")\n","project = rf.workspace(\"robocon-zvbcr\").project(\"robocon-00ofb\")\n","dataset = project.version(6).download(\"yolov8\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2928,"status":"ok","timestamp":1708428451468,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"IogMaYNvkTMF","outputId":"a79c6111-f2d8-474b-fc1b-ae10d268978c"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2024-02-20 11:27:28--  https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt\n","Resolving github.com (github.com)... 140.82.112.3\n","Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/521807533/407a6ed1-e224-4534-8672-b3118af5125c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240220%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240220T112728Z&X-Amz-Expires=300&X-Amz-Signature=7362ced6a94cda932d1136ad77f151f16407d89da4a974d9cee151c135947e0a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=521807533&response-content-disposition=attachment%3B%20filename%3Dyolov8l.pt&response-content-type=application%2Foctet-stream [following]\n","--2024-02-20 11:27:28--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/521807533/407a6ed1-e224-4534-8672-b3118af5125c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240220%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240220T112728Z&X-Amz-Expires=300&X-Amz-Signature=7362ced6a94cda932d1136ad77f151f16407d89da4a974d9cee151c135947e0a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=521807533&response-content-disposition=attachment%3B%20filename%3Dyolov8l.pt&response-content-type=application%2Foctet-stream\n","Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n","Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 87769683 (84M) [application/octet-stream]\n","Saving to: ‘yolov8l.pt’\n","\n","yolov8l.pt          100%[===================>]  83.70M  61.2MB/s    in 1.4s    \n","\n","2024-02-20 11:27:30 (61.2 MB/s) - ‘yolov8l.pt’ saved [87769683/87769683]\n","\n"]}],"source":["!wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"e1Mph4PxjxaP","outputId":"b849e839-18e0-484e-8b09-2513b97e48b9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8l.pt, data=/content/drive/MyDrive/yolov8/Robocon-6/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train4, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train4\n","2024-02-20 11:43:56.623138: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-02-20 11:43:56.623202: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-02-20 11:43:56.624428: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","Overriding model.yaml nc=80 with nc=1\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n","  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n","  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n","  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n","  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n","  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n","  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n","  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n","  8                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           \n","  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n"," 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 12                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \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  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 \n"," 16                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n"," 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 18                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 \n"," 19                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n"," 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 21                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n"," 22        [15, 18, 21]  1   5583571  ultralytics.nn.modules.head.Detect           [1, [256, 512, 512]]          \n","Model summary: 365 layers, 43630611 parameters, 43630595 gradients, 165.4 GFLOPs\n","\n","Transferred 589/595 items from pretrained weights\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train4', view at http://localhost:6006/\n","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","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/yolov8/Robocon-6/train/labels... 423 images, 66 backgrounds, 0 corrupt: 100% 423/423 [00:04<00:00, 89.76it/s] \n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/yolov8/Robocon-6/train/labels.cache\n","WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 75, len(boxes) = 1776. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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/MyDrive/yolov8/Robocon-6/valid/labels... 40 images, 3 backgrounds, 0 corrupt: 100% 40/40 [00:00<00:00, 73.33it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/yolov8/Robocon-6/valid/labels.cache\n","WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 1, len(boxes) = 183. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n","Plotting labels to runs/detect/train4/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=0.002, momentum=0.9) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005), 103 bias(decay=0.0)\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1mruns/detect/train4\u001b[0m\n","Starting training for 50 epochs...\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       1/50        11G      1.224      2.072      1.187         39        640: 100% 27/27 [00:23<00:00,  1.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.95it/s]\n","                   all         40        183          0          0          0          0\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       2/50      11.3G      1.292      1.655      1.242         63        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.50it/s]\n","                   all         40        183    0.00293     0.0109    0.00116   0.000583\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       3/50      11.4G      1.279      1.089      1.267         43        640: 100% 27/27 [00:20<00:00,  1.30it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.80it/s]\n","                   all         40        183    0.00293     0.0109    0.00116   0.000583\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       4/50      11.4G      1.375      1.212      1.268         44        640: 100% 27/27 [00:20<00:00,  1.31it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.65it/s]\n","                   all         40        183    0.00293     0.0109    0.00116   0.000583\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       5/50      11.4G      1.336      1.014      1.273         32        640: 100% 27/27 [00:20<00:00,  1.32it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.53it/s]\n","                   all         40        183    0.00293     0.0109    0.00116   0.000583\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       6/50      11.4G      1.317      1.087      1.242         39        640: 100% 27/27 [00:20<00:00,  1.29it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.33it/s]\n","                   all         40        183    0.00293     0.0109    0.00116   0.000583\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       7/50      11.4G      1.275     0.9875       1.26         36        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.12it/s]\n","                   all         40        183      0.613      0.147      0.157      0.119\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       8/50      11.5G      1.208     0.8483      1.198         32        640: 100% 27/27 [00:20<00:00,  1.29it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.31it/s]\n","                   all         40        183      0.858      0.672      0.727       0.46\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       9/50      11.3G       1.22     0.9002       1.22         25        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.88it/s]\n","                   all         40        183      0.753      0.705      0.727      0.474\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      10/50      11.4G      1.182     0.8462      1.198         48        640: 100% 27/27 [00:21<00:00,  1.24it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.22it/s]\n","                   all         40        183      0.905      0.675      0.774      0.489\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      11/50      11.5G      1.156     0.7704      1.176         67        640: 100% 27/27 [00:22<00:00,  1.21it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.11it/s]\n","                   all         40        183      0.826      0.639      0.705      0.419\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      12/50      11.4G      1.137     0.8475      1.207         23        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.69it/s]\n","                   all         40        183      0.851      0.748      0.784      0.463\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      13/50      11.3G      1.141     0.7988      1.193         33        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.75it/s]\n","                   all         40        183      0.894      0.732      0.805      0.506\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      14/50      11.5G       1.16     0.7675      1.169         37        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.81it/s]\n","                   all         40        183      0.923       0.72      0.812      0.522\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      15/50      11.5G      1.111     0.7373      1.142         51        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.21it/s]\n","                   all         40        183      0.918      0.738      0.808      0.511\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      16/50      11.3G      1.082     0.7504      1.165         23        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.83it/s]\n","                   all         40        183      0.907      0.748      0.826      0.517\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      17/50      11.4G      1.097     0.7052      1.135         53        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.63it/s]\n","                   all         40        183       0.94      0.738      0.845      0.535\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      18/50      11.4G      1.128     0.7228      1.146         34        640: 100% 27/27 [00:22<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.61it/s]\n","                   all         40        183      0.941      0.691      0.835      0.529\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      19/50      11.4G      1.072     0.6785      1.123         26        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.27it/s]\n","                   all         40        183      0.949      0.707      0.815      0.532\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      20/50      11.3G      1.044     0.6418      1.101         27        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.43it/s]\n","                   all         40        183      0.971      0.727      0.834      0.546\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      21/50      11.4G       1.05     0.6371      1.137         41        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.73it/s]\n","                   all         40        183      0.907      0.743       0.81      0.521\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      22/50      11.5G      1.114      0.716      1.145         41        640: 100% 27/27 [00:21<00:00,  1.25it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.75it/s]\n","                   all         40        183      0.922      0.754      0.839      0.522\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      23/50      11.4G      1.047     0.6574       1.11         41        640: 100% 27/27 [00:21<00:00,  1.25it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.61it/s]\n","                   all         40        183      0.921      0.743      0.839      0.533\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      24/50      11.3G      1.031     0.6211      1.128         39        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.22it/s]\n","                   all         40        183      0.923      0.754       0.81      0.528\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      25/50      11.5G      1.003     0.6104       1.11         24        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.32it/s]\n","                   all         40        183      0.901      0.747      0.815      0.539\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      26/50      11.5G      1.015     0.6049      1.122         48        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.34it/s]\n","                   all         40        183      0.885      0.797      0.833      0.546\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      27/50      11.5G      1.027     0.5848      1.095         28        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.14it/s]\n","                   all         40        183      0.893       0.77      0.845      0.538\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      28/50      11.3G       1.01     0.5618      1.073         33        640: 100% 27/27 [00:21<00:00,  1.25it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.27it/s]\n","                   all         40        183      0.899      0.781      0.857      0.539\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      29/50      11.4G     0.9579     0.5623      1.075         23        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.65it/s]\n","                   all         40        183      0.936      0.732       0.83      0.548\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      30/50      11.4G     0.9996     0.5552      1.062         28        640: 100% 27/27 [00:21<00:00,  1.25it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.25it/s]\n","                   all         40        183      0.865      0.771      0.836      0.553\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      31/50      11.5G     0.9787     0.5617      1.087         44        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.04it/s]\n","                   all         40        183      0.902      0.754      0.837       0.55\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      32/50      11.4G     0.9514       0.56      1.088         39        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.23it/s]\n","                   all         40        183       0.92      0.757      0.832      0.532\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      33/50      11.4G      0.974     0.5307       1.07         48        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.77it/s]\n","                   all         40        183      0.944      0.743      0.844       0.54\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      34/50      11.4G     0.9671     0.5188      1.058         43        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.30it/s]\n","                   all         40        183      0.949      0.765      0.807      0.534\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      35/50      11.5G     0.9668     0.5086      1.086         38        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.59it/s]\n","                   all         40        183       0.92       0.77      0.857      0.559\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      36/50      11.5G     0.8985     0.4863      1.049         26        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.21it/s]\n","                   all         40        183      0.945      0.755      0.851      0.559\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      37/50      11.5G     0.9007     0.4872      1.044         48        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.69it/s]\n","                   all         40        183      0.919      0.798      0.871      0.561\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      38/50      11.5G     0.9055      0.495      1.049         64        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.27it/s]\n","                   all         40        183       0.91      0.774      0.839      0.553\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      39/50      11.4G     0.9223     0.4887      1.044         30        640: 100% 27/27 [00:21<00:00,  1.23it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.27it/s]\n","                   all         40        183      0.896      0.756      0.842      0.551\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      40/50      11.4G     0.9157     0.4814       1.06         23        640: 100% 27/27 [00:21<00:00,  1.27it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.36it/s]\n","                   all         40        183       0.94      0.738      0.835      0.549\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   cls_loss   dfl_loss  Instances       Size\n","      41/50      11.4G     0.8921     0.4422      1.058         22        640: 100% 27/27 [00:23<00:00,  1.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.33it/s]\n","                   all         40        183      0.912      0.803       0.85      0.564\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      42/50      11.5G     0.9087     0.4673      1.067         13        640: 100% 27/27 [00:21<00:00,  1.24it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.74it/s]\n","                   all         40        183       0.88      0.805      0.873      0.568\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      43/50      11.5G     0.9071     0.4476       1.08         16        640: 100% 27/27 [00:21<00:00,  1.24it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.64it/s]\n","                   all         40        183      0.953       0.76      0.873      0.585\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      44/50      11.4G     0.8947     0.4394      1.056         22        640: 100% 27/27 [00:21<00:00,  1.24it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.22it/s]\n","                   all         40        183      0.935      0.791      0.867      0.571\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      45/50      11.3G      0.869     0.4334      1.058         13        640: 100% 27/27 [00:21<00:00,  1.26it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.66it/s]\n","                   all         40        183      0.929      0.803      0.867      0.579\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      46/50      11.5G     0.8643     0.4139      1.053         28        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.28it/s]\n","                   all         40        183      0.902      0.803       0.86      0.579\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      47/50      11.4G     0.8625     0.3997      1.054         28        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.25it/s]\n","                   all         40        183      0.917      0.781      0.864      0.574\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      48/50      11.5G     0.8666     0.4086      1.044         34        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.71it/s]\n","                   all         40        183      0.896      0.776      0.858      0.564\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      49/50      11.5G     0.8492     0.3889      1.029         23        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.93it/s]\n","                   all         40        183      0.928       0.77      0.858       0.57\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      50/50      11.4G     0.8295     0.3872      1.012         32        640: 100% 27/27 [00:21<00:00,  1.28it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  2.11it/s]\n","                   all         40        183      0.942       0.76      0.859      0.572\n","\n","50 epochs completed in 0.389 hours.\n","Optimizer stripped from runs/detect/train4/weights/last.pt, 87.7MB\n","Optimizer stripped from runs/detect/train4/weights/best.pt, 87.7MB\n","\n","Validating runs/detect/train4/weights/best.pt...\n","Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","Model summary (fused): 268 layers, 43607379 parameters, 0 gradients, 164.8 GFLOPs\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.78it/s]\n","                   all         40        183      0.953       0.76      0.873      0.584\n","Speed: 0.3ms preprocess, 18.7ms inference, 0.0ms loss, 2.6ms postprocess per image\n","Results saved to \u001b[1mruns/detect/train4\u001b[0m\n","💡 Learn more at https://docs.ultralytics.com/modes/train\n"]}],"source":["!yolo task=detect mode=train model=yolov8l.pt data=/content/drive/MyDrive/yolov8/Robocon-6/data.yaml epochs=50 imgsz=640"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sB9dA4pESdof"},"outputs":[],"source":["Image(filename=f'/content/drive/MyDrive/ball.png', width=600)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mBb--Qs4Sxyi"},"outputs":[],"source":["!yolo task=segment mode=val model=/content/runs/segment/train3/weights/best.pt data={dataset.location}/data.yaml"]},{"cell_type":"markdown","metadata":{"id":"pw-wZEZZUeXv"},"source":["##Predictions with Trained YOLOv8 Model on Custom Dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8821,"status":"ok","timestamp":1708429277801,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"dVgEOxfXUdof","outputId":"59e21817-5f55-40c0-942d-25d8b54be217"},"outputs":[{"name":"stdout","output_type":"stream","text":["Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","YOLOv8l-seg summary (fused): 295 layers, 45912659 parameters, 0 gradients, 220.1 GFLOPs\n","\n","image 1/1 /content/drive/MyDrive/yolov8/ball.png: 384x640 2 balls, 116.2ms\n","Speed: 3.4ms preprocess, 116.2ms inference, 826.5ms postprocess per image at shape (1, 3, 384, 640)\n","Results saved to \u001b[1mruns/segment/predict5\u001b[0m\n","💡 Learn more at https://docs.ultralytics.com/modes/predict\n"]}],"source":["!yolo task=segment mode=predict model=/content/drive/MyDrive/yolov8/runs/segment/train3/weights/best.pt conf=0.4 source=/content/drive/MyDrive/yolov8/ball.png"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13735,"status":"ok","timestamp":1708437248825,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"jH1vsNlgkHJK","outputId":"97604630-3ad1-484c-c282-ae5ab0ac9561"},"outputs":[{"output_type":"stream","name":"stdout","text":["Ultralytics YOLOv8.1.16 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n","YOLOv8l summary (fused): 268 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs\n","\n","image 1/1 /content/drive/MyDrive/yolov8/ball3.jpg: 544x640 11 sports balls, 1 baseball glove, 1 apple, 2705.0ms\n","Speed: 14.1ms preprocess, 2705.0ms inference, 3.0ms postprocess per image at shape (1, 3, 544, 640)\n","Results saved to \u001b[1mruns/detect/predict5\u001b[0m\n","💡 Learn more at https://docs.ultralytics.com/modes/predict\n"]}],"source":["!yolo task=detect mode=predict model=/content/drive/MyDrive/yolov8/yolov8l.pt conf=0.2 source=/content/drive/MyDrive/yolov8/ball3.jpg"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23708,"status":"ok","timestamp":1708524229313,"user":{"displayName":"Kiều Duyên Nguyễn","userId":"14053125906439003950"},"user_tz":-420},"id":"g3JaGflPupKz","outputId":"7f0e8bda-9607-4251-9ec4-500e6a65bdbf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6AxgUfEJU_sE"},"outputs":[],"source":["import glob\n","from IPython.display import Image, display\n","\n","from image_path in glob.glob(f'')[:5]\n","    display(Image(filename=image_path, height=600))\n","    print(\"\\n\")"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
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