Mixup and Cutmix

The Pytorch Image Model (timm) framework provides an option to use Mixup and Cutmix augmentations. We can use these techniques to enhance the performance of our model.

Mixup

Mixup is a domain-agnostic augmentation technique. It randomly generates weighted combinations of image pairs from the training data. It takes two images and their corresponding ground truths to generate a new image.

The implementation for Mix up looks like this:

$$\tilde{x} = \lambda x_i + (1 - \lambda) x_j$$

$$\tilde{y} = \lambda y_i + (1 - \lambda) y_j$$ ...