qidanrui / hashnet

Code release for "HashNet: Deep Learning to Hash by Continuation" (ICCV 2017)

HashNet

Code release for "HashNet: Deep Learning to Hash by Continuation" (ICCV 2017)

Datasets

We use ImageNet, NUS-WIDE and COCO dataset in our experiments. You can download the ImageNet dataset and NUS-WIDE dataset here. As for COCO dataset, we use COCO 2014, which can be downloaded here. And in case of COCO changes in the future, we also provide a download link here on google drive. After downloading, you need to move the imagenet.tar.gz to ./data/imagenet and extract the file there.

mv imagenet.tar.gz ./data/imagenet
cd ./data/imagenet
tar -zxvf imagenet.tar.gz

Also, for NUS-WIDE, you need to move the nus_wide.tar.gz to ./data/nuswide_81 and extract the file there.

mv nus_wide.tar.gz ./data/nuswide_81
cd ./data/nuswide_81
tar -zxvf nus_wide.tar.gz

For COCO dataset, you need to extract both train and val archive for COCO in ./data/coco. If you download from COCO download page,

mv train2014.zip ./data/coco
mv val2014.zip ./data/coco
cd ./data/coco
unzip train2014.zip
unzip val2014.zip

If you use our shared link

mv coco.tar.gz ./data/coco
cd ./data/coco
tar -zxvf coco.tar.gz
unzip train2014.zip
unzip val2014.zip

You can also modify the list file in ./data as you like. Each line in the list file follows the following format:

image_name label_represented_by_0_and_1

Compiling

The compiling process is the same as caffe. You can refer to Caffe installation instructions here.

Training

You can train the model for each dataset using the followling command.

dataset_name = imagenet, nuswide_81 or coco
./build/tools/caffe train -solver models/train/dataset_name/solver.prototxt -weights ./models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu gpu_id

For more instructions about training and parameter setting, see the instructions in the training directory.

Evaluation

You can evaluate the Mean Average Precision(MAP) result on each dataset using the followling command.

dataset_name = imagenet, nuswide_81 or coco
python models/predict/dataset_name/predict_parallel.py --gpu gpu_id --model_path your_caffemodel_path --save_path the_path_to_save_your_code

For more instructions about training and parameter setting, see the instructions in the predicting directory.

Citation

If you use this code for your research, please consider citing:

@article{cao2017hashnet,
  title={HashNet: Deep Learning to Hash by Continuation},
  author={Cao, Zhangjie and Long, Mingsheng and Wang, Jianmin and Yu, Philip S},
  journal={arXiv preprint arXiv:1702.00758},
  year={2017}
}