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Glenn Jocher 2018-09-01 14:04:42 +02:00
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@ -24,17 +24,17 @@ Run `train.py` to begin training after downloading COCO data with `data/get_coco
## Image Augmentation
`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.
`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.
Augmentation | Description
--- | ---
Translation | +/- 20% vertical and horizontal
Rotation | +/- 5 degrees
Skew | +/- 3 degrees
Scale | +/- 20%
Reflection | 50% probability left-right
Saturation | +/- 50%
Intensity | +/- 50%
Translation | +/- 20% vertical and horizontal
Rotation | +/- 5 degrees
Shear | +/- 3 degrees vertical and horizontal
Scale | +/- 20%
Horizontal Reflection | 50% probability
H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50%
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_augmentation_examples.jpg "coco image augmentation")