This code implement the binGAN method, that was presented in Zieba M., Semberecki P. El-Gaaly T., Trzcinski T.. "BinGAN: Learning Compact Binary Descriptors with a Regularized GAN." arxiv

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The training code requires Lasagne. Using GPU is highly advised.

Go into the Github repository directory.

To build Docker image, perform commands:

docker build --build-arg USER_UID=$(id -u) --build-arg USER_GID=$(id -g) -t lasagne_img .

To run docker container instance with GPU support:

nvidia-docker run -it --name lasagne_gpu --user lasagne -v $(pwd):/home/lasagne/host/binGAN lasagne_img bash

The docker container already has the git repository, however you can mount you own host directory by adding -v option, for example /home/user/binGAN:/home/lasagne/host/binGAN to have results/datasets saved independently to container.

The procedure of training binGan is run with train_model.py script.

There are two datasets for two tasks in which binGAN is evaluated:

• Brown dataset (image matching)
• Cifar10 (image retrieval)

Selecting the proper dataset is performed in settings.py, by setting proper value of dataset_type parameter. In this file it is possible also to set various type of training hyperparameters described in details in the paper.

Brown dataset, which is composed of three subsets: yosemite, notredame and liberty. It is crucial to specify, which training and validation set by setting proper values to parameters: data_name and test_data.

It is crucial to set proper value of binary features (num_features), while switching between datasets.

The quality of image matching on Brown dataset is evaluated by script test_brown.py

The quality of image retrieval on Cifar10 dataset is evaluated by script test_cifar10.py