<p>You don't have to worry about data augmentation too much because it has a number of data augmentation techniques that it applies to help improve model performance and generalization.</p>
<p>A few of the data augmentation techniques used by YoloV4 are <code>CutMix, Blurring, Class label smoothing, Mosaic data augmentation, Self-Adversarial Training</code>. You can learn more about the different types and impact of various augmentation methods from the YoloV4 paper <a href="https://arxiv.org/pdf/2004.10934.pdf" rel="nofollow noreferrer">here</a></p>
<p><a href="https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-%5Bnet%5D-section" rel="nofollow noreferrer">Here</a> is the list of all the data augmentation techniques used in <a href="https://github.com/AlexeyAB/darknet" rel="nofollow noreferrer">this</a> GitHub implementation of YoloV4</p>
Data augmentation with bounding boxes
<p>I want to train an object detection model using YOLO v4. I have a folder containing jpg images with the bounding boxes annotations in a txt file. I don't have much data so I decided to do some data augmentation on my data. I faced the following problems :</p>
<ol>
<li>I have tried Roboflow so I can have directly the bounding boxes in txt files, but the problem is that Roboflow applies randomly data augmentation and sometimes it gives the same picture or applies only a small change.</li>
<li>I have tried Albumentations but I had problems with the bounding boxes, I tried the Pascal voc format and it worked but I didn't know how to do it automatically for the whole dataset.</li>
</ol>
<p>Is there any other solutions or suggestions, I will be grateful.
Thank you</p>