I am trying to train an object detection model in Yolov8 using ultralytics. I have a dataset with 8975 images, a total of 28 classes and 26616 annotations. Note that each image can have different annotated classes. The big problem arises when I want to perform a data augmentation only of the most unbalanced classes, but without increasing a lot the number of classes with more annotations or at least taking them into account. This is the distribution class:
- Class 1 9253 annot
- Class 2 6537 annot
- Class 3 1777 annot
- Class 4 1512 annot
- Class 5 1416 annot
- Class 6 1341 annot
- Class 7 1177 annot
- Class 8 785 annot
- Class 9 625 annot
- Class 10 388 annot
- Class 11 369 annot
- Class 12 282 annot
- Class 13 216 annot
- Class 14 155 annot
- Class 15 128 annot
- Class 16 93 annot
- Class 17 92 annot
- Class 18 84 annot
- Class 19 81 annot
- Class 20 72 annot
- Class 21 66 annot
- Class 22 60 annot
- Class 23 51 annot
- Class 24 33 annot
- Class 25 15 annot
- Class 26 4 annot
- Class 27 2 annot
- Class 28 2 annot
Some Help!
I need a code or library that I can use so that from a folder containing images (.jpg) and annotations, I can increase the number of images contained in the minority classes, without exceeding too much the other classes.