{"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\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\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","execution":{"iopub.status.busy":"2023-06-17T07:45:10.004025Z","iopub.execute_input":"2023-06-17T07:45:10.004515Z","iopub.status.idle":"2023-06-17T07:45:12.706092Z","shell.execute_reply.started":"2023-06-17T07:45:10.004471Z","shell.execute_reply":"2023-06-17T07:45:12.704953Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Reference\n\nData exploration\nhttps://www.kaggle.com/code/ihelon/hubmap-exploratory-data-analysis\n\nBackground Knowledge\nhttps://www.kaggle.com/code/omarsobhy14/eda-understanding-the-human-vasculature\n\n\nYOLOv5 tutorial\nhttps://www.kaggle.com/code/maxkav/yolov5-tutorial-train-with-custom-data\n\n\n## Plan\n\nStart with YOLOv5... if workable -> YOLOv8 (YOLOv5 maybe have chatGPT support)\n\ntho YOLOv8 is under development\n\na YOLOv8 example\nhttps://www.kaggle.com/code/alabibojesomo/inference-yolo\n","metadata":{}},{"cell_type":"markdown","source":"## install YOLOv5\n\n### Dataset\n\n**Anotation:**\npolygons.jsonl","metadata":{}},{"cell_type":"code","source":"!git clone https://github.com/ultralytics/yolov5\n!pip install -r yolov5/requirements.txt","metadata":{"execution":{"iopub.status.busy":"2023-06-17T08:06:46.539095Z","iopub.execute_input":"2023-06-17T08:06:46.539501Z","iopub.status.idle":"2023-06-17T08:07:01.151824Z","shell.execute_reply.started":"2023-06-17T08:06:46.539470Z","shell.execute_reply":"2023-06-17T08:07:01.150516Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import yolov5\n","metadata":{},"execution_count":null,"outputs":[]}]}