https://github.com/chenshen03/deephash-tensorflow
DeepHash框架，包括DQN、DHN、DVSQ、DCH、DTQ等方法。
https://github.com/chenshen03/deephash-tensorflow
deephash hash tensorflow
Last synced: about 1 year ago
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DeepHash框架，包括DQN、DHN、DVSQ、DCH、DTQ等方法。
- Host: GitHub
- URL: https://github.com/chenshen03/deephash-tensorflow
- Owner: chenshen03
- Created: 2019-05-17T15:36:01.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-01-18T02:03:20.000Z (over 5 years ago)
- Last Synced: 2025-03-29T04:05:30.608Z (about 1 year ago)
- Topics: deephash, hash, tensorflow
- Language: Python
- Homepage:
- Size: 180 KB
- Stars: 12
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DeepHash-tensorflow
This code repository is derived from [thulab/DeepHash](https://github.com/thulab/DeepHash) but adds more features.
See the commits and update logs for details.
## Update logs:
- Metrics
- add WhRank
- add mAP_finetune
- add RAMAP
- Backbone
- add VGG-F network
- Loss functions
- add Independent loss
- add balance loss
- add orthogonal loss
- add exp loss
- add quantization losses, *i.e.*, L1 loss、L2 loss、cauchy loss
- Tools
- add visualization tool such as T-SNE, *etc*
- add distance libs
- Others
- refactoring the `data_provider` code
- refactoring the `model` code
- refactoring `parser` code
- training acceleration
## Contacts
Maintainers of this library:
* Shen Chen, Email: chenshen@stu.xmu.edu.cn
---
DeepHash is a lightweight deep learning to hash library that implements state-of-the-art deep hashing/quantization algorithms. We will implement more representative deep hashing models continuously according to our released [deep hashing paper list](https://github.com/caoyue10/DeepHashingBaselines). Specifically, we welcome other researchers to contribute deep hashing models into this toolkit based on our framework. We will announce the contribution in this project.
The implemented models include:
* DQN: [Deep Quantization Network for Efficient Image Retrieval](http://yue-cao.me/doc/deep-quantization-networks-dqn-aaai16.pdf), 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](http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-hashing-network-aaai16.pdf), Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao, AAAI Conference on Artificial Intelligence (AAAI), 2016
* DVSQ: [Deep Visual-Semantic Quantization for Efficient Image Retrieval](http://yue-cao.me/doc/deep-visual-semantic-quantization-cvpr17.pdf), Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
* DCH: [Deep Cauchy Hashing for Hamming Space Retrieval](http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-cauchy-hashing-cvpr18.pdf), Yue Cao, Mingsheng Long, Bin Liu, Jianmin Wang, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
* DTQ: [Deep Triplet Quantization](ise.thss.tsinghua.edu.cn/~mlong/doc/deep-triplet-quantization-acmmm18.pdf), Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang, ACM Multimedia (ACMMM), 2018
Note: DTQ and DCH are updated while DQN, DHN, DVSQ maybe outdated, feel free to touch us if you have any questions. We welcome others to contribute!
## Requirements
- Python3: Anaconda is recommended because it already contains a lot of packages:
```
conda create -n DeepHash python=3.6 anaconda
source activate DeepHash
```
- Other packages:
```
conda install -y tensorflow-gpu
conda install -y -c conda-forge opencv
```
To import the pakcages implemented in `./DeepHash`, we need to add the path of `./DeepHash` to environment variables as:
```shell
export PYTHONPATH=/path/to/project/DeepHash/DeepHash:$PYTHONPATH
```
## Data Preparation
In `data/cifar10/train.txt`, we give an example to show how to prepare image training data. In `data/cifar10/test.txt` and `data/cifar10/database.txt`, the list of testing and database images could be processed during predicting procedure. If you want to add other datasets as the input, you need to prepare `train.txt`, `test.txt` and `database.txt` as CIFAR-10 dataset.
What's more, We have put the whole cifar10 dataset including the images and data list in the [release page](https://github.com/thulab/DeepHash/releases/download/v0.1/cifar10.zip). You can directly download it and unzip to data/cifar10 folder.
Make sure the tree of `/path/to/project/data/cifar10` looks like this:
```
.
|-- database.txt
|-- test
|-- test.txt
|-- train
`-- train.txt
```
If you need run on NUSWIDE_81 and COCO, we recommend you to follow https://github.com/thuml/HashNet/tree/master/pytorch#datasets to prepare NUSWIDE_81 and COCO images.
For *DVSQ* model, you also need the *word vector* of the semantic labels. Here we use word2vec model pretrained on GoogleNews Dataset (e.g. https://github.com/mmihaltz/word2vec-GoogleNews-vectors), to extract the word embeddings for the labels of images, e.g. dog, cat and so on.
## Get Started
### Pre-trained model
You should manually download the model file of the Imagenet pre-tained AlexNet from [here](https://github.com/thulab/DeepHash/releases/download/v0.1/reference_pretrain.npy.zip) or from release page and unzip it to `/path/to/project/DeepHash/architecture/pretrained_model`.
Make sure the tree of `/path/to/project/DeepHash/architecture` looks like this:
```
├── __init__.py
├── pretrained_model
└── reference_pretrain.npy
```
### Training and Testing
The example of `$method` (DCH and DTQ) can be run like:
```shell
cd example/$method/
python train_val_script.py --gpus "0,1" --data-dir $PWD/../../data --"other parameters descirbe in train_val_script.py"
```
For DVSQ, DQN and DHN, please refer to the `train_val.sh` and `train_val_script.py` in the examples folder.
## Citations
If you find *DeepHash* is useful for your research, please consider citing the following papers:
@InProceedings{cite:AAAI16DQN,
Author = {Yue Cao and Mingsheng Long and Jianmin Wang and Han Zhu and Qingfu Wen},
Publisher = {AAAI},
Title = {Deep Quantization Network for Efficient Image Retrieval},
Year = {2016}
}
@InProceedings{cite:AAAI16DHN,
Author = {Han Zhu and Mingsheng Long and Jianmin Wang and Yue Cao},
Publisher = {AAAI},
Title = {Deep Hashing Network for Efficient Similarity Retrieval},
Year = {2016}
}
@InProceedings{cite:CVPR17DVSQ,
Title={Deep visual-semantic quantization for efficient image retrieval},
Author={Cao, Yue and Long, Mingsheng and Wang, Jianmin and Liu, Shichen},
Booktitle={CVPR},
Year={2017}
}
@InProceedings{cite:CVPR18DCH,
Title={Deep Cauchy Hashing for Hamming Space Retrieval},
Author={Cao, Yue and Long, Mingsheng and Bin, Liu and Wang, Jianmin},
Booktitle={CVPR},
Year={2018}
}
@article{liu2018deep,
title={Deep triplet quantization},
author={Liu, Bin and Cao, Yue and Long, Mingsheng and Wang, Jianmin and Wang, Jingdong},
journal={MM, ACM},
year={2018}
}