Is 1000 images enough to train cnn for image retouching and colour correction?

I have a collection of 1000 (2.5k x 2.5k) images of jewellery on white background (my family business). Images are in 3 states - 1. unmodified jpg from camera, 2. retouched photo (dust and scratches, background removal), 3. plus colour correction (white balance, contrast, light, stone colour correction).

I’m thinking of trying to train a cnn to do 2. and 3. stage of image retouching, maybe all in one.

Any suggestions? Do I have enough data, which architecture to use, transfer learning, augmentation, other strategies? Has anyone done something similar? I don’t have big budget for training.

My knowledge is limited to Udemy nano class and all the videos. I’m just starating to code DL, but I’m full stack web dev, so coding I like :slight_smile:

Lesson 7 course 1 would provide you with the details of how to build it. Seeing that the images are of so high resolution, you can split the images into maybe 3x3 grid and then do the random crop and other transforms.

The data you have is more than enough to perform this task.