MMHH

The Pytorch implementation of Maximum-Margin Hamming Hashing.

Requirements

The code requires some common packages:

# python>=3.6
# Anaconda: it is not necessary but recommended since it contains a lot of common packages.
conda create -n py36 python=3.6 
source activate py36

# pytorch = 0.4.1
conda install pytorch=0.4.1 cuda92 -c pytorch

# Maybe you need tensorboardX for detailed analysis
pip install tensorboardX

Data Preparation

We recommend you to follow HashNet to prepare the dataset images.

Our paper also conducts experiments on noise data and Unseen Classes Retrieval Protocol. The preprocessing has been elaborated in the paper and is easy to follow. We will add the preprocessing scripts soon.

Example Usage

To train the model, it is an example:

python train_mmhh.py --gpu_id=0 --s_dataset="coco_80" --hash_bit=48 --annotation="MMHH-train" --loss_lambda=0.001 --num_iters=1000 --image_network="AlexNetFc" --batch_size=48 --radius 2 --distance_type "MMHH" --similar_weight "1"  --lr 0.0001 --decay_step 200  --gamma 10.0 --opt-test True

The model will be examined in the end of training. If you want to test a model individually, run the following example:

python test_mmhh.py --gpu_id=0 --dataset="coco_80" --model_path "../snapshot/hash/MMHH-train_coco_80_coco_80_iter_01000" --batch_size=48 --radius 2 --opt-test --annotation="MMHH-test" --test_sample_ratio 1.0

The basic metric functions refer to DeepHash (MAP@H<=2) and HashNet (MAP@TopK). We optimize them carefully and speed up by ×2 to ×10.

Due to the numpy randomness, the optimized version may be slightly different from the original ones, but we believe it doesn't matter after lots of tests.

Acknowledgments

Our code mainly refers to the following repositories, we want to thanks for their invaluable help sincerely:

• HashNet : the dataset, data processing, the network backbones, etc..
• DeepHash: the DCH implementation and the training parameters.
• Snca.pytorch: the augmented memory.

Citations

If you find the codes are helpful to your work, please kindly cite our paper:

@inproceedings{DBLP:conf/iccv/Kang0L0Y19,
  author    = {Rong Kang and
               Yue Cao and
               Mingsheng Long and
               Jianmin Wang and
               Philip S. Yu},
  title     = {Maximum-Margin Hamming Hashing},
  booktitle = {2019 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
               2019, Seoul, Korea (South), October 27 - November 2, 2019},
  pages     = {8251--8260},
  publisher = {{IEEE}},
  year      = {2019},
}

If you encounter any issues, please feel free to send an email to kangr15@mails.tsinghua.edu.cn. We will do our best to address your concerns.