This is a caffe repository for learning to hash. We fork the repository from hash-caffe and make our modifications. The main modifications are listed as follow:
- Change the deep learning network model from Alexnet(2012) to Googlenet(2014)
- Change the weight which quantization_loss weighs to get a better performance
After downloads this git, you can install this project by editing "Makefile.config" and using following command:
"./make all"
(requiring for CUDNN 7.5 or later versions).
We use GPU while computing, so you need to prepare one if you want to test this project on your own.
In data/adspro2/train.txt, we give an example to show how to prepare training data.Normally,you have to conform to following format:
[File/Path] [Labels]
In which the length of labels is the total categories of your dataset,and use 1 or 0 to represent if one Pic belongs to one category or not.
In data/nus_wide/parallel/, the list of testing and database images are splitted to 12 parts, which could be processed parallelly when predicting.
In models/GOOGLE_DHN/, we give an example to show how to train hash model. In this model, we use pairwise loss and quantization loss as loss functions.
The bvlc_googlenet_caffenet is used as the pre-trained model. If the NUS_WIDE dataset and pre-trained caffemodel is prepared, the example can be run with the following command:
"./build/tools/caffe train -solver models/GOOGLE_DHN/solver.prototxt -weights models/GOOGLE_DHN/bvlc_googlenet.caffemodel"
In pairwise loss layer and quantization loss layer, parameter loss_weight can be tuned to give them different weights.
In models/GOOGLE_DHN/predict_parallel.py, we give an example to show how to evaluate the trained hash model.
If you want to test the caffemodel on a single Pic, you can just edit the "make_database.py"(mainly about the "params.pretrained_model") and run the following command:
"python predict_single_pic.py [Test_File_Path]"