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ultralytics 8.0.108 add Meituan YOLOv6 models (#2811)

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Co-authored-by: Michael Currie <mcurrie@gmail.com>
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Co-authored-by: Hicham Talaoubrid <98521878+HichTala@users.noreply.github.com>
Co-authored-by: Zlobin Vladimir <vladimir.zlobin@intel.com>
Co-authored-by: Szymon Mikler <sjmikler@gmail.com>
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Glenn Jocher 2023-05-25 00:43:32 +02:00 • committed by GitHub
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@ -93,7 +93,7 @@ model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX format
path = model.export(format="onnx") # export the model to ONNX format
```
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.