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73 lines
2.6 KiB
73 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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