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