I have a question for you that maybe you could give me a clue.
I'm trying to make a CBIR (Content-Based Image Retrieval), so I query an image and I get the most similar, not classified within a database of around 2000 images. I'm trying to extract features and matching them by euclidean distance but I was going to try to train them with the framework called Caffe, for deep learning (neural networks).
So, what I've done is taking 10 samples per every image by 3d projection (so I take the image 635.jpg and I make 635_1.jpg and so on to 635_10.jpg) and I use the original image 635.jpg as validation data. So if there are 2000 images, I use 20.000 images for training and 2.000 images for validation into 2.000 clases... do you think it's a good idea?
Thank you in advance.
Although I've been trying, I wasn't still able to train on Caffe due to the high quantity of errors in the dependencies on Mac OSx. I will switch to Ubuntu and see what's wrong.