Skip to content
A caffe version implementation of a hash network(DNNH/NINH) for similarity-based visual research based on paper: Simultaneous feature learning and hash coding with deep neural networks
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
cmake
data
docker
docs
examples
include/caffe
matlab
models
python
runtime
scripts
src
tools
.gitattributes
.gitignore
CMakeLists.txt
CONTRIBUTING.md
CONTRIBUTORS.md
INSTALL.md
LICENSE
Makefile
Makefile.config
Makefile.config.example
README.md
YupanHuang-Similarity-Based-Visual-Search.pdf
caffe.cloc

README.md

This is a caffe version implementation of a hash network(DNNH/NINH) for similarity-based visual research.

The hash network is based on this paper: Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. Simultaneous feature learning and hash coding with deep neural networks, CVPR 2015.

For more details about the motivation, approaches, implementation, results&analysis and further improvements, please read my post. Any feedback is welcome!

My work

  • Deploy: Given the definition of loss layer, deploy the deep hashing pipeline on linux.
  • Train: Write prototxt to define dnnh and bash files to execute for training on preprocessed triplet CIFAR-10 dataset.
  • Test/Evaluate: Write prototxt to encode images and bash files to execute for image retrieval. Implement the metric of mean average precision (mAP) for evaluation.
  • Analysis: Draw performances for 12-bit, 24-bit and 48-bit hash code and make some analysis.
  • Presentation: Prepare a slide to show my work.

How to run

Dataset

Hash training needs triplet data input. Here I use the triplet CIFAR-10 dataset. To obtain it:

  • You can directly download the related zip file cifar_hash_dataset.7z from BaiduYun and extract it into caffe-dnnh\runtime\cifar_hash_dataset.
  • Or you can process the data by yourself. Scripts are provided for reference in caffe-dnnh\runtime\cifar_hash_dataset_process_scripts\.

Deploy

You may directly download my caffe-dnnh zip and deploy (may need to fix errors due to different environment and version). Or you can follow the instructions to add files/contents to the newest caffe release. Here CAFFE-ROOT refers to your root caffe directory and caffe-dnnh to mine.

  1. Add file caffe-dnnh/src/caffe/layers/triplet_ranking_hinge_loss_layer.cpp to path CAFFE-ROOT/src/caffe/layers and file caffe-dnnh/include/caffe/layers/triplet_ranking_hinge_loss_layer.hpp to path CAFFE-ROOT/include/caffe/layers.
  2. Modify file CAFFE-ROOT/src/caffe/proto/caffe.proto:
    • Add the following code directly.
// Message that stores parameters used by TripletRankingHingeLossLayer
message TripletRankingHingeLossParameter{
   //Dimension for computing
   optional int32 dim = 1 [default = 10];
   //Margin
   optional float margin = 2 [default = 1];
}
  • Find message LayerParameter, add optional TripletRankingHingeLossParameter triplet_ranking_hinge_loss_param = 151; in it.
  • Find message V1LayerParameter, add optional TripletRankingHingeLossParameter triplet_ranking_hinge_loss_param = 43; in it.
  • Find enum LayerType in message V1LayerParameter, add TRIPLET_RANKING_HINGE_LOSS=40; in it.

Attention: the number above like 151, 43 are ID and should not be conflict with others. Search next available in caffe.proto you will find comment like // SolverParameter next available ID: 42 (last added: layer_wise_reduce) and // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param). Use next available ID and update the comment. 3. Add folder caffe-dnnh/runtime to path CAFFE-ROOT/. 4. Modify file CAFFE-ROOT/tools/caffe.cpp refer to caffe-dnnh/tools/caffe.cpp: Search ++++++++++ in caffe-dnnh/tools/caffe.cpp and you will find what I add.

Attention: For CPU/GPU mode switch

  1. check CPU_ONLY := 1 in CAFFE-ROOT/Makefile.config
  2. In folder CAFFE-ROOT/runtime/: check solver_mode: GPU in all solver.prototxt files (e.g. CAFFE-ROOT/runtime/12bit/train12_solver.prototxt), check -gpu=0 in all run_test.sh files (e.g. CAFFE-ROOT/runtime/12bit/run_test.sh)

Then follow the official Installation instructions to compile. Good luck!

Train

cd caffe-dnnh/runtime/12bit # or: 24bit, 48bit
sh ./run_train.sh # or: sh ./resume_train.sh

run_train.sh train deep hash neural network defined in prototxt and result models are stored in path caffe-dnnh/runtime/model. You can modify parameters like max iteration, snapshot in solver prototxt. Also note that tens of thousands iterations take time, so you are recommended to train with GPU mode in the background like nohup sh ./run_train.sh & and check output with command tail -100 nohup.out. Read corresponding files for more details.

Test

cd caffe-dnnh/runtime/12bit # or: 24bit, 48bit
sh ./run_test.sh

run_test.sh: uses forward pass of dnnh defined in test12_query.prototxt and test12_pool.prototxt to encode query images and pool set images. Then compile and run CAFFE-ROOT/runtime/evaluate_map.cpp for image retrieval evaluation. You can modify parameters (e.g. ITER in run_test.sh and top_neighbor_num in evaluate_map.cpp). Read corresponding files for more details.

Credits

I really appreciate their works!

  1. Dr.Tao Mei draw an outline of this research for me.
  2. The triplet ranking hinge loss layer is implemented by @FuchenUSTC in his caffe repository.
  3. Preprocessed triplet CIFAR-10 dataset and related scripts are shared by @FuchenUSTC. Read my post#dataset for more details about its structure so as to understand the structure of DNNH defined in prototxt.
  4. Networks structure and parameters are refered to codes_triplet_hashing1.zip provide by first author Hanjiang Lai.
You can’t perform that action at this time.