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Getting Started with Image Classification with PyTorch

RandAugment

Explore how to use the RandAugment class in PyTorch to apply various random image transformations that diversify datasets and help improve model training. Understand the parameters that control magnitude, layers, and probability to fine-tune augmentations for better image classification results.

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The Pytorch Image Model framework provides another useful augmentation class called RandAugment. As the name implies, it performs random augmentations on the image datasets.

The RandAugment class

Here’s a list of image transformations in the RandAugment class:

Transformation Description
Invert Invert the colors of the input image.
AutoContrast Maximize the contrast of the input image by remapping its pixels per channel.
Equalize Equalize the histogram of the input image.
Rotate Rotate the input image by a certain angle.
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