Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

RandomCutMix, RandomMixUp augmentation

See original GitHub issue

These two well-known and very effective augmentation methods are widely used among ML practitioners. In keras, we can find some general augmentation . And these two augmentation methods can be included as advanced augmentation layers.

CutMix - Paper - Cited by ~ 865 MixUp - Paper - Cited by ~ 2675

Let’s add these to keras_cv.layers.RandomCutMix and keras_cv.layers.RandomMixUp.

Issue Analytics

State:
Created 2 years ago
Comments:17 (8 by maintainers)

Top GitHub Comments

2reactions

LukeWoodcommented, Dec 4, 2021

Thanks @innat! I’ll take a look at this early next week.

1reaction

bhackcommented, Dec 3, 2021

We could port these from: https://github.com/tensorflow/models/blob/master/official/vision/beta/ops/augment.py#L20

Top Results From Across the Web

CutMix, MixUp, and RandAugment image augmentation with ...

CutMix randomly cuts out portions of one image and places them over another, and MixUp interpolates the pixel values between two images. Both...

Mixup & CutMix Augmentations | timmdocs

In this tutorial we will be looking specifically into the various training arguments to implement MixUp and CutMix augmentations during training and also...

CutMix: A new strategy for Data Augmentation

In CutMix augmentation we cut and paste random patches between the training images. The ground truth labels are mixed in proportion to the...

Modern Data Augmentation Techniques for Computer Vision

This article gives a comparison between Cutout, mixup, CutMix, ... Simple image data augmentation techniques like flipping, random crop, ...

Cutmix.. vs Mixup.. vs Gridmask ..vs Cutout... - Kaggle

This notebook give the overview of how to perform cutmix, mixup, Gridmask and Cutout data augmentation. And analyse the performance of AUgmentation.