Tensorflow implementation of a supervised approach to learn highly compressed image representations
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LICENSE.md
README.md
ReadDataset.py
dependencies.txt
subic.png
test_subic.py
tf_nets.py
tf_utility.py
train_subic.py
utility.py

README.md

SuBiC: A supervised, structured binary code for image search

subic

Tensorflow implementation of our paper at ICCV 2017, which proposes a supervised approach to learn highly compressed image representations.

To train:

It requires a path_file with paths of all training images along with their label ids.

To train from a pre-trained base-CNN and fine-tuning only the new layers, as we do in the paper, run: ipython train_subic --m 8 --k 256 --nclass 1000 --img_path path_file --pretrained trained_model --skip_last 2 --finetune 2

Or for a full training run: ipython train_subic --m 8 --k 256 --nclass num_classes --img_path path_file

Check the arguments in train_subic.py to try different parameters and settings.

The path_file should look like,

path/to/imagenet/images/000001.jpg 0
path/to/imagenet/images/000002.jpg 4

Download a SuBiC model trained on ImageNet will be available soon (links will be set on this page). It has 8 layers, the first 7 layers are of VGG_M_128 with weights from this caffe model.

To test:

For a quick test we provide VGG_M_128 features for pascalvoc images with labels, download from [link available soon]. Then, add the paths of the downloaded files in test_subic.py and run: ipython test_subic trained_model VGG_M_128 --m 8 --k 256 --testset pascalvoc_features

To test with settings we used in the paper, you need to put the path file(s) for images and labels in test_subic.py corresponding to their datasets. Otherwise, directly run as: ipython test_subic trained_model VGG_M_128 --m 8 --k 256 --testset any_dataset --nclass num_classes --split split_size --N dataset_size --img_path path_file

License

by downloading this program, you commit to comply with the license as stated in the LICENSE.md file.

Contact

author: Jain Himalaya email: see ICCV 2017 paper for email adress.