Thanks for the feedback.
I’m trying to adapt this implementation https://github.com/flyingpot/pytorch_deephash 17 from a 2015 method for image retrieval, now using it for a multilabel dataset (the NUS-WIDE). I’m using the same dataset preprocessing used in this implementation https://github.com/jiangqy/ADSH-AAAI2018/tree/master/ADSH_pytorch 24 which gets an approximately 80% mAP, however it implements a specific function of its method.
Briefly, what an image retrieval method that uses deep learning does is take the representation of the image generated by the network, it applies some kind of binarization (in the case of this method it does a simple rounding up or down to generate codes of 0s and 1s), then use this code to calculate similarity between the images to make retrieval.
I’ve already checked the dataloader and it looks okay, but now I’ll check again with more caution. If you have any suggestions, I thank you.