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37 lines
1.2 KiB
37 lines
1.2 KiB
[](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml) |
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### Install |
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```bash |
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pip install ultralytics |
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``` |
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Development |
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``` |
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git clone https://github.com/ultralytics/ultralytics |
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cd ultralytics |
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pip install -e . |
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``` |
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## Usage |
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### 1. CLI |
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To simply use the latest Ultralytics YOLO models |
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```bash |
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yolo task=detect mode=train model=yolov8n.yaml args=... |
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classify predict yolov8n-cls.yaml args=... |
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segment val yolov8n-seg.yaml args=... |
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export yolov8n.pt format=onnx |
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``` |
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### 2. Python SDK |
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To use pythonic interface of Ultralytics YOLO model |
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```python |
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from ultralytics import YOLO |
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model = YOLO.new('yolov8n.yaml') # create a new model from scratch |
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model = YOLO.load('yolov8n.pt') # load a pretrained model (recommended for best training results) |
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results = model.train(data='coco128.yaml', epochs=100, imgsz=640, ...) |
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results = model.val() |
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results = model.predict(source='bus.jpg') |
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success = model.export(format='onnx') |
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``` |
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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.
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