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README.md
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README.md
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@ -24,15 +24,17 @@ Run `train.py` to begin training after downloading COCO data with `data/get_coco
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## Augmentation
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## Augmentation
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`datasets.py` applies random augmentation to the input images in accordance with the following specifications. Augmentation is applied *only* during training, not during inference. Bounding boxes are automatically tracked and updated with the images. Examples pictured below.
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`datasets.py` applies random augmentation to the input images in accordance with the following specifications. Augmentation is applied *only* during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below.
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- Translation: +/- 20% X and Y
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Augmentation | Description
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- Rotation: +/- 5 degrees
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--- | ---
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- Skew: +/- 3 degrees
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Translation | +/- 20% vertical and horizontal
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- Scale: +/- 20%
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Rotation | +/- 5 degrees
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- Reflection: 50% probability left-right
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Skew | +/- 3 degrees
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- Saturation: +/- 50%
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Scale | +/- 20%
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- Intensity: +/- 50%
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Reflection | 50% probability left-right
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Saturation | +/- 50%
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Intensity | +/- 50%
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![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_augmentation_examples.jpg "coco image augmentation")
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![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_augmentation_examples.jpg "coco image augmentation")
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