Caffe-cvprw15

Deep Learning of Binary Hash Codes for Fast Image Retrieval

Created by Kevin Lin, Huei-Fang Yang, and Chu-Song Chen at Academia Sinica, Taipei, Taiwan.

Introduction

We present a simple yet effective deep learning framework to create the hash-like binary codes for fast image retrieval. We add a latent-attribute layer in the deep CNN to simultaneously learn domain specific image representations and a set of hash-like functions. Our method does not rely on pairwised similarities of data and is highly scalable to the dataset size. Experimental results show that, with only a simple modification of the deep CNN, our method improves the previous best retrieval results with 1% and 30% retrieval precision on the MNIST and CIFAR-10 datasets, respectively. We further demonstrate the scalability and efficacy of the proposed approach on the large-scale dataset of 1 million shopping images.

The details can be found in the following CVPRW 2015 paper

Citing the deep hashing works

If you find our works useful in your research, please consider citing:

Deep Learning of Binary Hash Codes for Fast Image Retrieval
K. Lin, H.-F. Yang, J.-H. Hsiao, C.-S. Chen
CVPR Workshop (CVPRW) on Deep Learning in Computer Vision, DeepVision 2015, June 2015.


Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search
K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, C.-S. Chen
ACM International Conference on Multimedia Retrieval, ICMR 2015, June 2015.

CIFAR10 retrieval results

Performance comparison of different hashing methods on CIFAR10 dataset. The table shows the mean average precision (mAP) of top 1000 returned images with respect to different number of hash bits.

Prerequisites

0. MATLAB (tested with 2012b on 64-bit Linux)
1. Caffe's prerequisites

Install Caffe-cvprw15

Adjust Makefile.config and simply run the following commands:

$ make all -j8
$ make test -j8
$ make runtest -j8
$ make matcaffe
$ ./download_model.sh

For a faster build, compile in parallel by doing make all -j8 where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).

Demo

This demo generates 48-bits binary codes using our model trained on CIFAR10.

Launch matlab and run demo.m

>> demo

Retrieval evaluation on CIFAR10

First, run script prepare_eval.sh to download and setup CIFAR10 dataset.

$ ./prepare_eval.sh

Second, launch matalb and run run_cifar10.m to perform the evaluation of precision at k and mean average precision at k. We set k=1000 in the experiments. The bit length of binary codes is 48. This process takes around 12 minutes.

>> run_cifar10

Then, you will get the mAP result as follows.

>> MAP = 0.897373

Moreover, simply run the following commands to generate the precision at k curves:

$ cd analysis
$ gnuplot plot-p-at-k.gnuplot 

You will reproduce the precision curves with respect to different number of top retrieved samples when the 48-bit hash codes are used in the evaluation:

Train your own model on CIFAR10

First, run script prepare_train.sh to download ImageNet pretrained model and convert CIFAR10 dataset to leveldb format. The whole process takes around 5 minutes.

$ ./prepare_train.sh

Then, go to the folder /examples/cvprw15-cifar10, and run the training script:

$ cd /examples/cvprw15-cifar10
$ chmod 777 train_48.sh
$ ./train_48.sh

The training process takes roughly 5~6 hours on a desktop with GTX Titian Black GPU. You will finally get your model named KevinNet_CIFAR10_48_iter_xxxxxx.caffemodel

To use your model, modify the model_file in demo.m to link to your model:

    model_file = './YOUR/MODEL/PATH/filename.caffemodel';

Launch matlab, run demo.m and enjoy!

>> demo

Train your own model on another dataset

It should be easy to train the model using another dataset as long as that dataset has label annotations. You need to convert the dataset into leveldb format using "create_imagenet.sh". We will show you how to do this. To be continued.

Correction of computational cost

In previous experiments, we use mex-file to call C/C++ functions from MATLAB, which slows down the process. We improve the search with pure C/C++ implementation as shown below.

Performing the Euclidean distance measure between two 4096-dimensional vectors takes 22.6 μs. Computing hamming distance between two 64-bit binary codes takes 23 ps (bitwise XOR operation). Thus, the proposed method is around ~982,600x faster than traditional exhaustive search with 4096-dimensional features.

Resources

Note: This documentation may contain links to third party websites, which are provided for your convenience only. Third party websites may be subject to the third party’s terms, conditions, and privacy statements.

If the automatic "fetch_data" fails, you may manually download the resouces from:

0. For ./download_model.sh:

   • 48-bit model: MEGA, DropBox, BaiduYun

1. For ./prepare_eval.sh:

   • CIFAR10 dataset (jpg format): MEGA, DropBox, BaiduYun

2. For ./prepare_train.sh:

   • ImageNet-pretrained networks: MEGA, DropBox, BaiduYun

Models with respect to different hash bits:

0. The proposed deep hashing models trained on CIFAR10:
   • 12-bit model: MEGA, BaiduYun
   • 12-bit deploy file: MEGA, BaiduYun
   • 16-bit model: MEGA, BaiduYun
   • 16-bit deploy file: MEGA, BaiduYun
   • 32-bit model: MEGA, BaiduYun
   • 32-bit deploy file: MEGA, BaiduYun
   • 48-bit model: MEGA, DropBox, BaiduYun
   • 48-bit deploy file: MEGA, BaiduYun
   • 64-bit model: MEGA, BaiduYun
   • 64-bit deploy file: MEGA, BaiduYun
   • 128-bit model: MEGA, BaiduYun
   • 128-bit deploy file: MEGA, BaiduYun

Frequently asked questions

Please refer this FAQs

Contact

Please feel free to leave suggestions or comments to Kevin Lin (kevinlin311.tw@iis.sinica.edu.tw), Huei-Fang Yang (hfyang@citi.sinica.edu.tw) or Chu-Song Chen (song@iis.sinica.edu.tw)