This repository provides a PyTorch implementation of a custom cutout augmentation technique that creates colorful squares to be applied directly to tensors during the training loop after data loading. This augmentation can be particularly useful for addressing problems related to occlusion in your dataset, supporting both object detection and image segmentation tasks. By applying colorful squares, you can encourage your model to learn more robust and accurate features, even when parts of the input are occluded.
Cutout augmentation is a technique used to regularize neural networks during training by masking out random sections of input data. This encourages the model to learn more robust and invariant features, which can be especially helpful for improving generalization. Traditional cutout involves masking out rectangular sections of the input images with solid color, but in this repository, we introduce a novel approach where colorful squares are used instead.
This repository's colorful cutout augmentation supports the following tasks:
Improve the generalization and robustness of your image classification models by introducing colorful squares that occlude parts of the input images.
Enhance the detection performance of your object detection models by augmenting the input images with colorful squares. This can help the model better handle occluded objects in the real world.
Augment your image segmentation dataset with colorful squares to assist the model in segmenting objects even when they are partially occluded.