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| .github | ||
| docs | ||
| tests | ||
| ultralytics | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| CITATION.cff | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| MANIFEST.in | ||
| mkdocs.yml | ||
| README.md | ||
| requirements.txt | ||
| setup.cfg | ||
| setup.py | ||
Install
pip install ultralytics
Development
git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e .
Usage
1. CLI
To simply use the latest Ultralytics YOLO models
yolo task=detect mode=train model=yolov8n.yaml args=...
classify predict yolov8n-cls.yaml args=...
segment val yolov8n-seg.yaml args=...
export yolov8n.pt format=onnx
2. Python SDK
To use pythonic interface of Ultralytics YOLO model
from ultralytics import YOLO
model = YOLO.new('yolov8n.yaml') # create a new model from scratch
model = YOLO.load('yolov8n.pt') # load a pretrained model (recommended for best training results)
results = model.train(data='coco128.yaml', epochs=100, imgsz=640, ...)
results = model.val()
results = model.predict(source='bus.jpg')
success = model.export(format='onnx')
If you're looking to modify YOLO for R&D or to build on top of it, refer to Using Trainer Guide on our docs.