I have 20,000 plus images of art (paintings, sculptures, jars, etc). My goal is creating a computer vision model that, from an input (image), identifies the exact same piece of art and returns its id, details, etc.
In my case, the sample is static, fixed, so the model will not ever "see" new images, which makes me think that overfitting perhaps be the most desirable thing for the model to achieved (this translates to heavy data augmentation and high number of epochs).
The selected programming language is a mix of R and Python (Keras). I took a sample of 2000 pictures of only squared paintings (there are rectangles, panoramic, etc), used color distance to classified them into smaller samples, and my idea for later is: once the input is assigned to a sub sample, then carry out another model which determines the exact piece of art.
I'm a junior, so I'd appreciate your advice on whether: 1)This workflow sounds suitable? 2)Overfitting issue.
