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74 lines
2.6 KiB
74 lines
2.6 KiB
## Install
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Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
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the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
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up-to-date version.
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!!! example "Pip install method (recommended)"
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```bash
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pip install ultralytics
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```
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!!! example "Git clone method (for development)"
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```bash
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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pip install -e '.[dev]'
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```
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See contributing section to know more about contributing to the project
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## Use with CLI
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The YOLO command line interface (CLI) lets you simply train, validate or infer models on various tasks and versions.
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CLI requires no customization or code. You can simply run all tasks from the terminal with the `yolo` command.
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!!! example
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=== "Syntax"
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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 args...
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```
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=== "Example training"
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```bash
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yolo detect train model=yolov8n.pt data=coco128.yaml device=0
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```
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=== "Example Multi-GPU training"
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```bash
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yolo detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
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```
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[CLI Guide](cli.md){ .md-button .md-button--primary}
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## Use with Python
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Python usage allows users to easily use YOLOv8 inside their Python projects. It provides functions for loading and
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running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that
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users can quickly implement object detection in their projects.
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Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or
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classification into their Python projects using YOLOv8.
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!!! example
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Use the model
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results = model.train(data="coco128.yaml", epochs=3) # train the model
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results = model.val() # evaluate model performance on the validation set
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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success = model.export(format="onnx") # export the model to ONNX format
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```
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[Python Guide](python.md){.md-button .md-button--primary}
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