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YOLOv8 Examples in Python\n\nThis repo contains YOLOv8 examples such as object detection, object tracking etc.\n\nSee also: https://github.com/ultralytics/ultralytics\n\n# Installing YOLOv8\nTo install YOLOv8 Python packages and CLI tool open a terminal and run:\n```\npip install ultralytics\n```\n\n# Double Check PATH\nTo use YOLOv8 CLI Tool Python Scripts folder should be added to PATH.\nFor Windows (Python Version is 3.11): `%APPDATA%\\Python\\Python311\\Scripts`\n\n# YOLOv8 Pretrained Models\n\nInstead of model.pt that trained for drones only you can type any YOLOv8 model\n```\nyolov8n.pt, yolov8s.pt, yolov8m.pt, yolov8l.pt or yolov8x.pt\n```\n\nSee https://docs.ultralytics.com/tasks/detect/#models\n\nYou don't need to manually download this model files they will be downloaded automatically from YOLOv8.\n\nBut this examples uses trained model for drone detection.\n\n# CLI Predict Images\n```\nyolo predict model=model.pt source=\"example-images/01.jpg\"\n```\nor multiple\n```\nyolo predict model=model.pt source=\"example-images/\\*.jpg\"\n```\n\n# CLI Predict Videos\n```\nyolo predict model=model.pt source=\"example-videos/01.mp4\"\n```\nor multiple\n```\nyolo predict model=model.pt source=\"example-videos/\\*.mp4\"\n```\n\n# CLI Traning Model\nAfter you select and prepare datasets (e.g. upload any dataset and then download for YOLOv8 from RoboFlow)\nyou can train the model with this command.\n\n```\nyolo task=detect mode=train model=yolov8n.pt data=dataset-folder/data.yaml epochs=20 imgsz=640\n```\n\nIt should fail if you don't edit .yaml file which has relative file paths. If so you should change\ntest: ..., train: ... paths with absolute paths. E.g. for Google Colab it should be `/content/My-Dataset/test`\nfor test folder instead of `./My-Dataset/test` or `My-Dataset/test`. \n\nAfter this task completed you can find trained models `best.pt` and `last.pt` in `runs/detect/weights` folder.\n\nIt is recommended to run training tasks in Google Colab or another service that ensures TPU or GPU. If your\ncomputer doesn't have CUDA with NVIDIA GPU or any supported TPU training task will be run at CPU and process\nwill be too much slow.\n\n# Results\nYou can see results in `runs/detect/predictX` folders after CLI command completed.\n\n# Python Examples\nYou can found python examples in folders next to this file.\n\n# License\nThe Unlicense. Feel free to use or change it how you need.\nBut third party sources like pictures, videos etc. may have some limitations.\nIf you have doubts, please check out the links we attributed.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fynsrc%2Fpython-yolov8-examples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fynsrc%2Fpython-yolov8-examples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fynsrc%2Fpython-yolov8-examples/lists"}