lxw4939 / dhn-caffe

[DEPRECATED] Please move to https://github.com/thuml/hash-caffe

DEPRECATED

This repository is deprecated, please move to hash-caffe

dhn-caffe

This is the implementation of AAAI paper "Deep Hashing Network for Efficient Similarity Retrieval". We fork the repository from Caffe and make our modifications. The main modifications are listed as follow:

• Change the label from a single integer to an array, whose length is the dimension of label.
• Add pairwise loss layer described in the paper

Training Model

In models/nus_wide, we give an example model based on Alexnet and NUS-WIDE dataset.

The bvlc_reference_caffenet is used as the pre-trained model.

Data Preparation

In data/nus_wide/train.txt, we give an example to show how to prepare the train data file. In this file, each image in NUS-WIDE has its label whose dimension is 21. The training set is extracted from the whole dataset as described in our paper.

Parameter Tuning

In ImageDataLayer, parameter label_dim should be set to tell the dimension of label. For example, label_dim: 21 for NUS-WIDE.

base_lr, stepsize and gamma in solver.prototxt can be tuned to achieve better performance. num_output of hash_layer in train_val.prototxt can be tuned to train with different hash bits. q_gamma in pairwise_loss layer can be tuned to set different loss weight for quantization loss.

Testing

After training from bvlc_reference_caffenet, a finetuned caffemodel can be used to generate hash codes for images in database and test samples. The python program in models/predict can help gengerating hash codes by given a file like train.txt.