{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n#    for filename in filenames:\n#        print(os.path.join(dirname, filename))\n!pwd\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","execution":{"iopub.status.busy":"2021-07-24T04:17:11.839882Z","iopub.execute_input":"2021-07-24T04:17:11.840212Z","iopub.status.idle":"2021-07-24T04:17:12.529275Z","shell.execute_reply.started":"2021-07-24T04:17:11.840127Z","shell.execute_reply":"2021-07-24T04:17:12.528390Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!git clone https://github.com/ultralytics/yolov5.git\n\n!cp ../input/yamlmunagedata/* /kaggle/working/yolov5\n#!ls ../input/yamlmunagedata\n#!mkdir /kaggle/working/yolov5/wheatdata\n!cp -rf ../input/wheatdata /kaggle/working/yolov5\n\n!ls /kaggle/working/yolov5\n#!rm -rf /kaggle/working/yolo5","metadata":{"execution":{"iopub.status.busy":"2021-07-24T04:17:12.531058Z","iopub.execute_input":"2021-07-24T04:17:12.531439Z","iopub.status.idle":"2021-07-24T04:17:49.836164Z","shell.execute_reply.started":"2021-07-24T04:17:12.531398Z","shell.execute_reply":"2021-07-24T04:17:49.835228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls /kaggle/working/yolov5/wheatdata\n!wandb off\n!python /kaggle/working/yolov5/train.py","metadata":{"execution":{"iopub.status.busy":"2021-07-24T04:17:49.838305Z","iopub.execute_input":"2021-07-24T04:17:49.838678Z","iopub.status.idle":"2021-07-24T05:06:27.813623Z","shell.execute_reply.started":"2021-07-24T04:17:49.838635Z","shell.execute_reply":"2021-07-24T05:06:27.812707Z"},"trusted":true},"execution_count":null,"outputs":[]}]}