Merge branch 'master' of https://github.com/thuml/DeepHash

Author: bl0

README.md

@@ -1,6 +1,6 @@
 ## Overall
 
-The xlearn hash learning library is a lightweight deep learning to hash library based on TensorFlow. This repository contains newly designed hashing and quantization methods, e.g. DHN, DQN and DVSQ. Besides, hash learning library provides high-level APIs and working examples for defining, training, fine-tuning and evaluating hashing models.
+The DeepHash package is a lightweight deep learning to hash library based on TensorFlow. This repository contains newly designed deep learning to hash and quantization methods, e.g. DHN, DQN, DVSQ and DCH. Besides, hash learning library provides high-level APIs and working examples for defining, training, fine-tuning and evaluating hashing models.
 
 ## Installation
 
@@ -23,33 +23,31 @@ For more details see *[TensorFlow installation instructions](https://github.com/
 
 To easily use our lib, we need to install scipy, python-opencv, h5py, scikit-learn, coverage.py and pytest by:
 
-```python
+```shell
 pip install scipy 
 sudo apt-get install python-opencv
 pip install h5py
 pip install -U scikit-learn
-pip install coverage
-pip install pytest
 ```
 
 To use the algorithms implemented in `./core`, we need to add the path of `./core` to environment variables as:
 
-```python
-export PYTHONPATH=/path/to/project/xlearn/hash_learning/core:$PYTHONPATH
+```shell
+export PYTHONPATH=/path/to/project/DeepHash/core:$PYTHONPATH
 ```             
 
 ## Data Preparation
 In `data/cifar10/train.txt`, we give an example to show how to prepare image training data. In `data/cifar10/test.txt` and `data/cifar10/database.txt`, the list of testing and database images could be processed during predicting procedure. If you want to add other datasets as the input, you need to prepare `train.txt`, `test.txt` and `database.txt` as CIFAR-10 dataset.
 
 ## Get Started
-The example of `$method` (DVSQ, DQN and DHN) can be run with the following command:
+The example of `$method` (DCH, DVSQ, DQN and DHN) can be run with the following command:
 ```
 cd example/$method/
 ./train_val.sh
 ```
 
 ## Core
-* `./core/model/`: contains the implementation of models: dhn, dqn and dvsq.
+* `./core/model/`: contains the implementation of models: dhn, dqn, dvsq and dch.
 * `./core/architecture/`: contains the implementation of network structure, e.g. AlexNet.
 * `./core/data_provider/`: contains the data reader implementation.
 * `./core/evaluation/`: contains the implementation of evaluation criteria in search procedure, such as mAP, precision, recall and so on.
@@ -59,11 +57,12 @@ cd example/$method/
 <!--**Evaluation**-->
 
 ## Methods
+* `DCH`: Deep Cauchy Hashing for Hamming Space Retrieval, Yue Cao, Mingsheng Long, Bin Liu, Jianmin Wang, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
 * `DVSQ`: Deep Visual-Semantic Quantization for Efficient Image Retrieval, Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
 * `DQN`: Deep Quantization Network for Efficient Image Retrieval, Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, Qingfu Wen, AAAI Conference on Artificial Intelligence (AAAI), 2016
 * `DHN`: Deep Hashing Network for Efficient Similarity Retrieval, Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao, AAAI Conference on Artificial Intelligence (AAAI), 2016
 
 ## Contacts
 Maintainers of hash learning library:
 * Yue Cao, Email: caoyue10@gmail.com
-* Bin Liu, Email: b\_liu14@163.com
+* Bin Liu, Email: liubinthss@gmail.com

core/model/dqn/dqn.py

@@ -1,6 +1,6 @@
 #################################################################################
-# Deep Visual-Semantic Quantization for Efficient Image Retrieval                #
-# Authors: Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu                    #
+# Deep Quantization Network for Efficient Image Retrieval                        #
+# Authors: Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, Qingfu Wen            #
 # Contact: caoyue10@gmail.com                                                    #
 ##################################################################################
 

core/model/prunehash/prunehash.py

@@ -1,6 +1,6 @@
 #################################################################################
-# Deep Hashing Network for Efficient Similarity Retrieval                        #
-# Authors: Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao                        #
+# Deep Cauchy Hashing for Hamming Space Retrieval                                #
+# Authors: Yue Cao, Mingsheng Long, Bin Liu, Jianmin Wang                        #
 # Contact: caoyue10@gmail.com                                                    #
 ##################################################################################