{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.8.16","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":6799,"databundleVersionId":4225553,"sourceType":"competition"}],"dockerImageVersionId":30497,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n \nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n    print(os.path.join(dirname))\n    break\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:49:22.463375Z","iopub.execute_input":"2024-11-13T19:49:22.463875Z","iopub.status.idle":"2024-11-13T19:49:23.789569Z","shell.execute_reply.started":"2024-11-13T19:49:22.463826Z","shell.execute_reply":"2024-11-13T19:49:23.788736Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"files = \"/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Annotations/CLS-LOC/train/n07614500\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:49:26.348286Z","iopub.execute_input":"2024-11-13T19:49:26.348667Z","iopub.status.idle":"2024-11-13T19:49:26.352508Z","shell.execute_reply.started":"2024-11-13T19:49:26.348642Z","shell.execute_reply":"2024-11-13T19:49:26.351829Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"!ls","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:49:27.41707Z","iopub.execute_input":"2024-11-13T19:49:27.417369Z","iopub.status.idle":"2024-11-13T19:49:28.516569Z","shell.execute_reply.started":"2024-11-13T19:49:27.417345Z","shell.execute_reply":"2024-11-13T19:49:28.515301Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"print('tes')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:49:28.518378Z","iopub.execute_input":"2024-11-13T19:49:28.518637Z","iopub.status.idle":"2024-11-13T19:49:28.522953Z","shell.execute_reply.started":"2024-11-13T19:49:28.518611Z","shell.execute_reply":"2024-11-13T19:49:28.522235Z"}},"outputs":[{"name":"stdout","text":"tes\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"import os\nimport subprocess\nfrom IPython.display import FileLink, display\n\ndef download_file(path, download_file_name):\n    os.chdir('/kaggle/working/')\n    zip_name = f\"/kaggle/working/{download_file_name}.zip\"\n    command = f\"zip {zip_name} {path} -r\"\n    result = subprocess.run(command, shell=True, capture_output=True, text=True)\n    if result.returncode != 0:\n        print(\"Unable to run zip command!\")\n        print(result.stderr)\n        return\n    display(FileLink(f'{download_file_name}.zip'))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:49:28.523894Z","iopub.execute_input":"2024-11-13T19:49:28.524141Z","iopub.status.idle":"2024-11-13T19:49:28.534469Z","shell.execute_reply.started":"2024-11-13T19:49:28.52412Z","shell.execute_reply":"2024-11-13T19:49:28.533795Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"def download_files(download_file_name):\n    os.chdir('/kaggle/working/')\n    files_list = []\n    for name in download_file_name:\n        for folder in ['Data','Annotations']:\n            files_list.append(\"/kaggle/input/imagenet-object-localization-challenge/ILSVRC/{}/CLS-LOC/train/{}\".format(folder,name))\n    files_list = \" \".join(files_list)\n#     print(files_list)\n    command = f\"zip /kaggle/working/testing.zip {files_list} -r\"\n    result = subprocess.run(command, shell=True, capture_output=True, text=True)\n    if result.returncode != 0:\n        print(\"Unable to run zip command!\")\n        print(result.stderr)\n        return\n    display(FileLink('testing.zip'))\n#     return files_list\n#     return command\n#     result = subprocess.run(command, shell=True, capture_output=True, text=True)\n","metadata":{"execution":{"iopub.status.busy":"2024-11-13T19:49:28.580262Z","iopub.execute_input":"2024-11-13T19:49:28.580986Z","iopub.status.idle":"2024-11-13T19:49:28.586493Z","shell.execute_reply.started":"2024-11-13T19:49:28.580956Z","shell.execute_reply":"2024-11-13T19:49:28.585708Z"},"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"download_files(['n07613480','n07614500','n07615774','n07693725','n07695742','n07697313','n07697537', 'n07711569','n07714571','n07714990','n07715103','n07720875','n07734744','n07745940','n07747607','n07749582','n07753275','n07753592','n07754684','n07760859','n07831146','n07873807','n12144580'])","metadata":{"execution":{"iopub.status.busy":"2024-11-11T19:47:57.128693Z","iopub.execute_input":"2024-11-11T19:47:57.129016Z","iopub.status.idle":"2024-11-11T19:47:57.142922Z","shell.execute_reply.started":"2024-11-11T19:47:57.128956Z","shell.execute_reply":"2024-11-11T19:47:57.141893Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Unable to run zip command!\n/bin/sh: 1: zip: not found\n\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"!pip install ultralytics opencv-python","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-11T19:47:57.638215Z","iopub.execute_input":"2024-11-11T19:47:57.63855Z","iopub.status.idle":"2024-11-11T19:48:07.452361Z","shell.execute_reply.started":"2024-11-11T19:47:57.638518Z","shell.execute_reply":"2024-11-11T19:48:07.451271Z"}},"outputs":[{"name":"stdout","text":"Collecting ultralytics\n  Downloading ultralytics-8.3.29-py3-none-any.whl (883 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.8/883.8 KB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting opencv-python\n  Downloading opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.8/site-packages (from ultralytics) (2.0.1)\nRequirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (2.31.0)\nRequirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.8/site-packages (from ultralytics) (9.5.0)\nRequirement already satisfied: psutil in /usr/local/lib/python3.8/site-packages (from ultralytics) (5.9.5)\nCollecting py-cpuinfo\n  Downloading py_cpuinfo-9.0.0-py3-none-any.whl (22 kB)\nRequirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (4.65.0)\nRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/site-packages (from ultralytics) (1.10.1)\nRequirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.8/site-packages (from ultralytics) (6.0)\nCollecting seaborn>=0.11.0\n  Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 KB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (0.15.1)\nCollecting ultralytics-thop>=2.0.0\n  Downloading ultralytics_thop-2.0.11-py3-none-any.whl (26 kB)\nRequirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (1.23.5)\nRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (3.7.1)\nRequirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.8/site-packages (from ultralytics) (2.0.0)\nRequirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (4.39.4)\nRequirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\nRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (1.0.7)\nRequirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (3.0.9)\nRequirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (5.12.0)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (0.11.0)\nRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (23.1)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.4)\nRequirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.8/site-packages (from pandas>=1.1.4->ultralytics) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.8/site-packages (from pandas>=1.1.4->ultralytics) (2023.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.8/site-packages (from requests>=2.23.0->ultralytics) (3.1.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/site-packages (from requests>=2.23.0->ultralytics) (2023.5.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.8/site-packages (from requests>=2.23.0->ultralytics) (1.26.16)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/site-packages (from requests>=2.23.0->ultralytics) (3.4)\nRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (4.6.0)\nRequirement already satisfied: nvidia-nccl-cu11==2.14.3 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (2.14.3)\nRequirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.91)\nRequirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (2.0.0)\nRequirement already satisfied: filelock in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.12.0)\nRequirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (8.5.0.96)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.1.2)\nRequirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.4.91)\nRequirement already satisfied: sympy in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (1.12)\nRequirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (10.2.10.91)\nRequirement already satisfied: networkx in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.1)\nRequirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.10.3.66)\nRequirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.99)\nRequirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (10.9.0.58)\nRequirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.4.0.1)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.99)\nRequirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.101)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.8/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.8.0->ultralytics) (57.5.0)\nRequirement already satisfied: wheel in /usr/local/lib/python3.8/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.8.0->ultralytics) (0.40.0)\nRequirement already satisfied: cmake in /usr/local/lib/python3.8/site-packages (from triton==2.0.0->torch>=1.8.0->ultralytics) (3.26.3)\nRequirement already satisfied: lit in /usr/local/lib/python3.8/site-packages (from triton==2.0.0->torch>=1.8.0->ultralytics) (16.0.5)\nRequirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib>=3.3.0->ultralytics) (3.15.0)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.2)\nRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\nInstalling collected packages: py-cpuinfo, opencv-python, seaborn, ultralytics-thop, ultralytics\nSuccessfully installed opencv-python-4.10.0.84 py-cpuinfo-9.0.0 seaborn-0.13.2 ultralytics-8.3.29 ultralytics-thop-2.0.11\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.3.1 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"from collections import defaultdict\n\nimport numpy as np\nfrom ultralytics import YOLO","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:50:24.858478Z","iopub.execute_input":"2024-11-13T19:50:24.858759Z","iopub.status.idle":"2024-11-13T19:50:45.724872Z","shell.execute_reply.started":"2024-11-13T19:50:24.85873Z","shell.execute_reply":"2024-11-13T19:50:45.723933Z"}},"outputs":[{"name":"stdout","text":"Creating new Ultralytics Settings v0.0.6 file ✅ \nView Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\nUpdate Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"!pip install opencv-python-headless ultralytics\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:50:14.725256Z","iopub.execute_input":"2024-11-13T19:50:14.725552Z","iopub.status.idle":"2024-11-13T19:50:24.857108Z","shell.execute_reply.started":"2024-11-13T19:50:14.725521Z","shell.execute_reply":"2024-11-13T19:50:24.85612Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.8/site-packages (4.7.0.72)\nCollecting ultralytics\n  Downloading ultralytics-8.3.29-py3-none-any.whl (883 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.8/883.8 KB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in 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already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (8.5.0.96)\nRequirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.4.91)\nRequirement already satisfied: filelock in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.12.0)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.1.2)\nRequirement already satisfied: networkx in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (3.1)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.99)\nRequirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (10.9.0.58)\nRequirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.99)\nRequirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.101)\nRequirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.8/site-packages (from torch>=1.8.0->ultralytics) (11.7.91)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.8/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.8.0->ultralytics) (57.5.0)\nRequirement already satisfied: wheel in /usr/local/lib/python3.8/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.8.0->ultralytics) (0.40.0)\nRequirement already satisfied: cmake in /usr/local/lib/python3.8/site-packages (from triton==2.0.0->torch>=1.8.0->ultralytics) (3.26.3)\nRequirement already satisfied: lit in /usr/local/lib/python3.8/site-packages (from triton==2.0.0->torch>=1.8.0->ultralytics) (16.0.5)\nRequirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib>=3.3.0->ultralytics) (3.15.0)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.2)\nRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\nInstalling collected packages: py-cpuinfo, opencv-python, seaborn, ultralytics-thop, ultralytics\nSuccessfully installed opencv-python-4.10.0.84 py-cpuinfo-9.0.0 seaborn-0.13.2 ultralytics-8.3.29 ultralytics-thop-2.0.11\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.3.1 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"!apt-get update && apt-get install ffmpeg libsm6 libxext6  -y\nimport cv2\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:50:51.970002Z","iopub.execute_input":"2024-11-13T19:50:51.970259Z","iopub.status.idle":"2024-11-13T19:50:54.980359Z","shell.execute_reply.started":"2024-11-13T19:50:51.970233Z","shell.execute_reply":"2024-11-13T19:50:54.979074Z"}},"outputs":[{"name":"stdout","text":"Hit:1 http://deb.debian.org/debian bullseye InRelease\nHit:2 http://deb.debian.org/debian-security bullseye-security InRelease\nHit:3 http://deb.debian.org/debian bullseye-updates InRelease\nReading package lists... Done\nReading package lists... Done\nBuilding dependency tree... Done\nReading state information... Done\nlibsm6 is already the newest version (2:1.2.3-1).\nlibxext6 is already the newest version (2:1.3.3-1.1).\nffmpeg is already the newest version (7:4.3.8-0+deb11u1).\n0 upgraded, 0 newly installed, 0 to remove and 120 not upgraded.\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"model = YOLO('yolov8n.pt')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:51:53.79948Z","iopub.execute_input":"2024-11-13T19:51:53.800319Z","iopub.status.idle":"2024-11-13T19:51:53.855215Z","shell.execute_reply.started":"2024-11-13T19:51:53.80028Z","shell.execute_reply":"2024-11-13T19:51:53.854289Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"import xml.etree.ElementTree as ET\nimport os\nfrom glob import glob\nimport numpy as np\n\ndef convert_annotation(xml_file, class_dict):\n    tree = ET.parse(xml_file)\n    root = tree.getroot()\n    \n    size = root.find('size')\n    width = int(size.find('width').text)\n    height = int(size.find('height').text)\n    \n    for obj in root.iter('object'):\n        difficult = obj.find('difficult').text\n        cls = obj.find('name').text\n        if cls not in class_dict:\n            continue\n        cls_id = class_dict[cls]\n        xmlbox = obj.find('bndbox')\n        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))\n        bb = convert_to_yolo_format((width, height), b)\n        return f\"{cls_id} {bb[0]} {bb[1]} {bb[2]} {bb[3]}\\n\"\n\ndef convert_to_yolo_format(size, box):\n    dw = 1./(size[0])\n    dh = 1./(size[1])\n    x = (box[0] + box[1])/2.0 - 1\n    y = (box[2] + box[3])/2.0 - 1\n    w = box[1] - box[0]\n    h = box[3] - box[2]\n    x = x*dw\n    w = w*dw\n    y = y*dh\n    h = h*dh\n    return (x,y,w,h)\n\n# Create a dictionary mapping class names to class IDs\nclass_dict = {}\nwith open('/kaggle/input/imagenet-object-localization-challenge/LOC_synset_mapping.txt', 'r') as f:\n    for i, line in enumerate(f):\n        class_dict[line.strip()] = i\n\n# Convert annotations\ninput_dir = '/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Annotations/CLS-LOC/train'\n# output_dir = '/kaggle/working/yolo_annotations'\n# os.makedirs(output_dir, exist_ok=True)\n\n# for xml_file in glob(f\"{input_dir}/**/*.xml\", recursive=True):\n#     yolo_annotation = convert_annotation(xml_file, class_dict)\n#     if yolo_annotation:\n#         base_name = os.path.splitext(os.path.basename(xml_file))[0]\n#         with open(f\"{output_dir}/{base_name}.txt\", 'w') as f:\n#             f.write(yolo_annotation)\n\n# print(\"Conversion complete!\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:52:05.086023Z","iopub.execute_input":"2024-11-13T19:52:05.086584Z","iopub.status.idle":"2024-11-13T19:52:05.113255Z","shell.execute_reply.started":"2024-11-13T19:52:05.08655Z","shell.execute_reply":"2024-11-13T19:52:05.112406Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"class_dict = {}\nwith open('/kaggle/input/imagenet-object-localization-challenge/LOC_synset_mapping.txt', 'r') as f:\n    for i, line in enumerate(f):\n        if i >= 30:  # Stop after 30 classes\n            break\n        parts = line.strip().split()\n        class_dict[parts[0]] = i\n\nprint(f\"Number of classes: {len(class_dict)}\")\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:52:08.952701Z","iopub.execute_input":"2024-11-13T19:52:08.95308Z","iopub.status.idle":"2024-11-13T19:52:08.959571Z","shell.execute_reply.started":"2024-11-13T19:52:08.953051Z","shell.execute_reply":"2024-11-13T19:52:08.958701Z"}},"outputs":[{"name":"stdout","text":"Number of classes: 30\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"class_dict","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:52:10.136075Z","iopub.execute_input":"2024-11-13T19:52:10.136412Z","iopub.status.idle":"2024-11-13T19:52:10.145673Z","shell.execute_reply.started":"2024-11-13T19:52:10.136388Z","shell.execute_reply":"2024-11-13T19:52:10.144815Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"{'n01440764': 0,\n 'n01443537': 1,\n 'n01484850': 2,\n 'n01491361': 3,\n 'n01494475': 4,\n 'n01496331': 5,\n 'n01498041': 6,\n 'n01514668': 7,\n 'n01514859': 8,\n 'n01518878': 9,\n 'n01530575': 10,\n 'n01531178': 11,\n 'n01532829': 12,\n 'n01534433': 13,\n 'n01537544': 14,\n 'n01558993': 15,\n 'n01560419': 16,\n 'n01580077': 17,\n 'n01582220': 18,\n 'n01592084': 19,\n 'n01601694': 20,\n 'n01608432': 21,\n 'n01614925': 22,\n 'n01616318': 23,\n 'n01622779': 24,\n 'n01629819': 25,\n 'n01630670': 26,\n 'n01631663': 27,\n 'n01632458': 28,\n 'n01632777': 29}"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"import os\nimport xml.etree.ElementTree as ET\nfrom glob import glob\nimport shutil\nfrom tqdm import tqdm\n\ndef convert_annotation(xml_file, class_dict):\n    tree = ET.parse(xml_file)\n    root = tree.getroot()\n    \n    size = root.find('size')\n    w = int(size.find('width').text)\n    h = int(size.find('height').text)\n    \n    results = []\n    for obj in root.iter('object'):\n        cls = obj.find('name').text\n        if cls not in class_dict:\n            continue\n        cls_id = class_dict[cls]\n        xmlbox = obj.find('bndbox')\n        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('ymin').text),\n             float(xmlbox.find('xmax').text), float(xmlbox.find('ymax').text))\n        bb = ((b[0]+b[2])/2/w, (b[1]+b[3])/2/h, (b[2]-b[0])/w, (b[3]-b[1])/h)\n        results.append(f\"{cls_id} {bb[0]} {bb[1]} {bb[2]} {bb[3]}\")\n    return '\\n'.join(results) + '\\n' if results else None\n\n# Setup directories\nbase_dir = '/kaggle/working/imagenet_yolo_30'\nos.makedirs(f'{base_dir}/images/train', exist_ok=True)\nos.makedirs(f'{base_dir}/labels/train', exist_ok=True)\nos.makedirs(f'{base_dir}/images/val', exist_ok=True)\nos.makedirs(f'{base_dir}/labels/val', exist_ok=True)\n\n# Process train set\ntrain_dirs = list(class_dict.keys())\nfor class_name in tqdm(train_dirs):\n    xml_files = glob(f'/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Annotations/CLS-LOC/train/{class_name}/*.xml')\n    for xml_file in xml_files:\n        yolo_annotation = convert_annotation(xml_file, class_dict)\n        if yolo_annotation:\n            base_name = os.path.splitext(os.path.basename(xml_file))[0]\n            # Write YOLO annotation\n            with open(f'{base_dir}/labels/train/{base_name}.txt', 'w') as f:\n                f.write(yolo_annotation)\n            # Copy image\n            shutil.copy(f'/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Data/CLS-LOC/train/{class_name}/{base_name}.JPEG',\n                        f'{base_dir}/images/train/{base_name}.jpg')\n\n# Process validation set\nval_xml_files = glob('/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Annotations/CLS-LOC/val/*.xml')\nfor xml_file in tqdm(val_xml_files):\n    yolo_annotation = convert_annotation(xml_file, class_dict)\n    if yolo_annotation:\n        base_name = os.path.splitext(os.path.basename(xml_file))[0]\n        # Write YOLO annotation\n        with open(f'{base_dir}/labels/val/{base_name}.txt', 'w') as f:\n            f.write(yolo_annotation)\n        # Copy image\n        shutil.copy(f'/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Data/CLS-LOC/val/{base_name}.JPEG',\n                    f'{base_dir}/images/val/{base_name}.jpg')\n\nprint(\"Conversion complete!\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-13T19:52:13.932415Z","iopub.execute_input":"2024-11-13T19:52:13.932738Z","iopub.status.idle":"2024-11-13T20:04:04.476076Z","shell.execute_reply.started":"2024-11-13T19:52:13.932711Z","shell.execute_reply":"2024-11-13T20:04:04.475273Z"}},"outputs":[{"name":"stderr","text":"100%|██████████| 30/30 [04:24<00:00,  8.80s/it]\n100%|██████████| 50000/50000 [07:23<00:00, 112.81it/s]","output_type":"stream"},{"name":"stdout","text":"Conversion complete!\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"yaml_content = f\"\"\"\npath: /kaggle/working/imagenet_yolo_30\ntrain: images/train\nval: images/val\n\nnc: {len(class_dict)}\nnames: {list(class_dict.keys())}\n\"\"\"\n\nwith open('/kaggle/working/imagenet_data_30.yaml', 'w') as f:\n    f.write(yaml_content)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T00:16:09.367373Z","iopub.execute_input":"2024-11-12T00:16:09.367828Z","iopub.status.idle":"2024-11-12T00:16:09.37376Z","shell.execute_reply.started":"2024-11-12T00:16:09.367793Z","shell.execute_reply":"2024-11-12T00:16:09.372981Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"from ultralytics import YOLO\n\n# Load a pretrained YOLOv8 model\nmodel = YOLO('yolov8n.pt')\n\n# Train the model\nresults = model.train(\n    data='/kaggle/working/imagenet_data_30.yaml',\n    epochs=100,\n    imgsz=640,\n    batch=16,\n    name='yolov8_imagenet_30'\n)\n\n# Validate the model\nresults = model.val()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-12T00:16:28.310499Z","iopub.execute_input":"2024-11-12T00:16:28.31144Z"}},"outputs":[{"name":"stdout","text":"Ultralytics 8.3.29 🚀 Python-3.8.16 torch-2.0.0+cu117 CPU (Intel Xeon 2.00GHz)\n\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=/kaggle/working/imagenet_data_30.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=yolov8_imagenet_30, 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=True, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/yolov8_imagenet_30\nDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 755k/755k [00:00<00:00, 46.5MB/s]\nD1112 00:17:02.192136991      15 config.cc:119]                        gRPC EXPERIMENT tcp_frame_size_tuning               OFF (default:OFF)\nD1112 00:17:02.192164715      15 config.cc:119]                        gRPC EXPERIMENT tcp_rcv_lowat                       OFF (default:OFF)\nD1112 00:17:02.192168218      15 config.cc:119]                        gRPC EXPERIMENT peer_state_based_framing            OFF (default:OFF)\nD1112 00:17:02.192170768      15 config.cc:119]                        gRPC EXPERIMENT flow_control_fixes                  ON  (default:ON)\nD1112 00:17:02.192173268      15 config.cc:119]                        gRPC EXPERIMENT memory_pressure_controller          OFF (default:OFF)\nD1112 00:17:02.192175885      15 config.cc:119]                        gRPC EXPERIMENT unconstrained_max_quota_buffer_size OFF (default:OFF)\nD1112 00:17:02.192178197      15 config.cc:119]                        gRPC EXPERIMENT new_hpack_huffman_decoder           ON  (default:ON)\nD1112 00:17:02.192182194      15 config.cc:119]                        gRPC EXPERIMENT event_engine_client                 OFF (default:OFF)\nD1112 00:17:02.192184634      15 config.cc:119]                        gRPC EXPERIMENT monitoring_experiment               ON  (default:ON)\nD1112 00:17:02.192187233      15 config.cc:119]                        gRPC EXPERIMENT promise_based_client_call           OFF (default:OFF)\nD1112 00:17:02.192189626      15 config.cc:119]                        gRPC EXPERIMENT free_large_allocator                OFF (default:OFF)\nD1112 00:17:02.192191987      15 config.cc:119]                        gRPC EXPERIMENT promise_based_server_call           OFF (default:OFF)\nD1112 00:17:02.192194383      15 config.cc:119]                        gRPC EXPERIMENT transport_supplies_client_latency   OFF (default:OFF)\nD1112 00:17:02.192196558      15 config.cc:119]                        gRPC EXPERIMENT event_engine_listener               OFF (default:OFF)\nI1112 00:17:02.192417087      15 ev_epoll1_linux.cc:122]               grpc epoll fd: 63\nD1112 00:17:02.192429677      15 ev_posix.cc:144]                      Using polling engine: epoll1\nD1112 00:17:02.192489014      15 dns_resolver_ares.cc:822]             Using ares dns resolver\nD1112 00:17:02.193078428      15 lb_policy_registry.cc:46]             registering LB policy factory for \"priority_experimental\"\nD1112 00:17:02.193090308      15 lb_policy_registry.cc:46]             registering LB policy factory for \"outlier_detection_experimental\"\nD1112 00:17:02.193094181      15 lb_policy_registry.cc:46]             registering LB policy factory for \"weighted_target_experimental\"\nD1112 00:17:02.193097268      15 lb_policy_registry.cc:46]             registering LB policy factory for \"pick_first\"\nD1112 00:17:02.193107444      15 lb_policy_registry.cc:46]             registering LB policy factory for \"round_robin\"\nD1112 00:17:02.193110651      15 lb_policy_registry.cc:46]             registering LB policy factory for \"weighted_round_robin_experimental\"\nD1112 00:17:02.193126857      15 lb_policy_registry.cc:46]             registering LB policy factory for \"ring_hash_experimental\"\nD1112 00:17:02.193148170      15 lb_policy_registry.cc:46]             registering LB policy factory for \"grpclb\"\nD1112 00:17:02.193237534      15 lb_policy_registry.cc:46]             registering LB policy factory for \"rls_experimental\"\nD1112 00:17:02.193271421      15 lb_policy_registry.cc:46]             registering LB policy factory for \"xds_cluster_manager_experimental\"\nD1112 00:17:02.193275106      15 lb_policy_registry.cc:46]             registering LB policy factory for \"xds_cluster_impl_experimental\"\nD1112 00:17:02.193278338      15 lb_policy_registry.cc:46]             registering LB policy factory for \"cds_experimental\"\nD1112 00:17:02.193288011      15 lb_policy_registry.cc:46]             registering LB policy factory for \"xds_cluster_resolver_experimental\"\nD1112 00:17:02.193291346      15 lb_policy_registry.cc:46]             registering LB policy factory for \"xds_override_host_experimental\"\nD1112 00:17:02.193294575      15 lb_policy_registry.cc:46]             registering LB policy factory for \"xds_wrr_locality_experimental\"\nD1112 00:17:02.193298882      15 certificate_provider_registry.cc:35]  registering certificate provider factory for \"file_watcher\"\nI1112 00:17:02.196952434      15 socket_utils_common_posix.cc:408]     Disabling AF_INET6 sockets because ::1 is not available.\nI1112 00:17:02.227880525    2027 socket_utils_common_posix.cc:337]     TCP_USER_TIMEOUT is available. TCP_USER_TIMEOUT will be used thereafter\nE1112 00:17:02.234132598    2027 oauth2_credentials.cc:236]            oauth_fetch: UNKNOWN:C-ares status is not ARES_SUCCESS qtype=A name=metadata.google.internal. is_balancer=0: Domain name not found {created_time:\"2024-11-12T00:17:02.234116192+00:00\", grpc_status:2}\n","output_type":"stream"},{"name":"stdout","text":"Overriding model.yaml nc=80 with nc=30\n\n                   from  n    params  module                                       arguments                     \n  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n 22        [15, 18, 21]  1    757162  ultralytics.nn.modules.head.Detect           [30, [64, 128, 256]]          \nModel summary: 225 layers, 3,016,698 parameters, 3,016,682 gradients, 8.2 GFLOPs\n\nTransferred 319/355 items from pretrained weights\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/yolov8_imagenet_30', view at http://localhost:6006/\nFreezing layer 'model.22.dfl.conv.weight'\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/imagenet_yolo_30/labels/train... 15849 images, 0 backgrounds, 0 corrupt: 100%|██████████| 15849/15849 [00:12<00:00, 1281.11it/s]\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/imagenet_yolo_30/labels/train.cache\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/imagenet_yolo_30/labels/val... 1500 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1500/1500 [00:01<00:00, 1249.58it/s]\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/working/imagenet_yolo_30/labels/val.cache\nPlotting labels to runs/detect/yolov8_imagenet_30/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 SGD(lr=0.01, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\nImage sizes 640 train, 640 val\nUsing 0 dataloader workers\nLogging results to \u001b[1mruns/detect/yolov8_imagenet_30\u001b[0m\nStarting training for 100 epochs...\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      1/100         0G     0.9432      3.757      1.364         21        640: 100%|██████████| 991/991 [25:07<00:00,  1.52s/it]\n                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 47/47 [01:17<00:00,  1.64s/it]","output_type":"stream"},{"name":"stdout","text":"                   all       1500       1929      0.279      0.378      0.285      0.194\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","output_type":"stream"},{"name":"stderr","text":"      2/100         0G      1.031      2.779      1.392         37        640:  89%|████████▉ | 880/991 [23:02<02:50,  1.54s/it]","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}