So keeping the initial 10 images of batch size 1 would have the dataloader’s length be 10. I want to essentially have my model have more instances of the initial images for my model to train on.
For example, maybe within the 10 initial images (dataloader of size 10), the model preformed transformation on one class (shift to the right), and that’s it for that class. But I want to add more instances of the same image, but transformed differently. I want there to instances of the class where it has been shifted to the left, or maybe upward, etc.
My ultimate goal is to make the training data larger than just 1 image per class so that there will be more variety for my network to work with per class. Would there be any way I can increase the amount of image per class, but with the data augmentation being randomized/different on those duplicate images from the same class?