在python深度学习中构建图像推荐系统的最佳模型

Question

I have explored about techniques to build Image Recommendation system with Deep Learning models, which it has to search in 100k images to find the top similar ones for recommendation on the given input image, I need the simple, best and reliable implementation references.

I tried with VGG-19 model didn't get expected results, not aware of other techniques.

Answer 1

There is a simpler method , which is similar to word embeddings. If we find an expressive vector representation, or embedding for images, we can then calculate their similarity by looking at how close their vectors are to each other.

In addition, if we calculate these vectors for all images in our database ahead of time, this approach is both fast (one forward pass, and an efficient similarity search), and scalable.

Annoy is one of the libraries that implement fast solutions.