CutMix is a data augmentation technique used in training deep learning models, particularly for image classification tasks. The main idea behind CutMix is to create new training samples by combining parts of two different images. Instead of simply rotating, flipping, or cropping an image, CutMix helps the model learn from more complex data scenarios, which can improve its generalization ability. It works by taking a patch from one image and pasting it onto another, while also modifying the corresponding labels to reflect the presence of both images in the mixed sample.
The process involves selecting a random bounding box on the first image, which defines the area that will be cut out. This cut-out patch is then placed onto a second image, leading to a mixed image that contains features of both original images. Importantly, the label of the new sample is calculated by taking a weighted average of the two original labels based on the area of the cut-out patch. For instance, if the cut-out patch covers 30% of the first image and the remaining 70% is from the second image, the final label would be a mix where 30% belongs to the first label and 70% to the second label.
Using CutMix can be particularly beneficial when working with smaller datasets or when trying to reduce overfitting. By introducing variations in the training data, the model becomes more robust and learns to recognize patterns even when some parts of the images are altered. For example, if a dog image is mixed with a car image, the model learns to identify features of both classes and can develop a more nuanced understanding of what defines each class, ultimately enhancing performance on unseen data.