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Ayush Chaurasia
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.github | 2 years ago | |
docs | 2 years ago | |
tests | 2 years ago | |
ultralytics | 2 years ago | |
.gitignore | 2 years ago | |
.pre-commit-config.yaml | 2 years ago | |
CITATION.cff | 2 years ago | |
CONTRIBUTING.md | 2 years ago | |
LICENSE | 2 years ago | |
MANIFEST.in | 2 years ago | |
README.md | 2 years ago | |
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requirements.txt | 2 years ago | |
setup.cfg | 2 years ago | |
setup.py | 2 years ago |
README.md
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 ...
classify predict yolov8n-cls.yaml
segment val yolov8n-seg.yaml
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.