I am training a deep learning model for object detection. The consensus is that the more images that you have, the better the results will be. All the tutorials that I have seen say that more images are key.
I am labeling objects in my images with Label-Img, which provides the algorithm with specific training samples on the images. For my images, I am using photos with dimensions of 1100 x 1100 pixels. In my case, I could generate anywhere between 50-100 high-quality training samples per image. For example:
In cases such as this where large numbers of training samples can be generated from a single image, do you really need several hundred images? Or can you lessen the number of images because of the number of training samples?