Zero-Shot-Hashing

Transductive Zero-Shot Hashing For Multi-Label Image Retrieval

This is the source code of Transductive Zero-Shot Hashing For Multi-Label Image Retrieval. We provide the codes, the partitioning of the datasets, and the pretrained models on between Nuswide and Voc.

Zou Q, Cao L, Zhang Z, Chen L, and Wang S, Transductive Zero-Shot Hashing For Multilabel Image Retrieval, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/tnnls.2020.3043298, Dec 2020.

Download:

You can also download the pretrained model from the following link,
Google Drive
https://drive.google.com/drive/folders/1pDqt9ZmyxxgUSOQo-Lu0uL6Tl3rz4wuV?usp=sharing
or Baidu Drive
https://pan.baidu.com/s/1y0A0rHaJcrbaPQzkfT_xNQ
passcodes：abcd

Set up

Requirements

PyTorch 1.0.0
Python 3.6
CUDA 10.0
We run on the Intel Core Xeon E5-2620@2.0GHz, 64GB RAM and two NVIDIA TITAN Xp GPUs.

Preparation

Data Preparation (training/test image indice)

Download the three public datasets and put them in the dataset/NUS-WIDE, VOC2012, COCO sub-folders, respectively. When you use some other datasets, you have to modify the values of the parameters unseen_img_dir and seen_img_dir.

In the folder `./data/', we have given the lists for partitioning the datasets for NUS-WIDE, VOC2012 and COCO. The indice of the training/test images can be found in the three subfolders.

For word2vec, the glove model is used. You can generate new word vectors by changing the path of the label file. ( codes for word2vec can be found in the `word2vec' folder)

Pretrained Models

Pretrained models using PyTorch are available using the link below, including the eight models, which take the ResNet50 as the backbone network, and are trained on between the Nuswide and Voc datasets, with 12-, 24-, 36-, and 48-bits hash codes.
You can download them and put them into "./models/".
Google Drive
https://drive.google.com/drive/folders/1pDqt9ZmyxxgUSOQo-Lu0uL6Tl3rz4wuV?usp=sharing
or Baidu Drive
https://pan.baidu.com/s/1y0A0rHaJcrbaPQzkfT_xNQ
passcodes：abcd

Training

Before training, you should set the right path to the datsaets you are working on, and then run:

python train_alexnet.py

or

python train_vgg.py

or

python train_resnet.py

Test

To evlauate the performance of a pre-trained model, you should put the pretrained model released by us or trained by yourself into "./models/" and change "pretrained_path" in the config.py at first, then choose the right model that is going to be evlauated and run:

python eval_alexnet.py

or

python eval_vgg.py

or

python eval_resnet.py

Citation:

Please cite our work if you use this code:

@article{zou2020tnnls,
  title={Transductive Zero-Shot Hashing For Multilabel Image Retrieval},
  author={Q. Zou and L. Cao and Z. Zhang and L. Chen and S. Wang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={DOI: 10.1109/tnnls.2020.3043298, Dec 2020},
}