This repository has the source code for the paper "Efficient end-to-end learning for quantizable representations"(ICML18).
@inproceedings{jeongICML18,
title={Efficient end-to-end learning for quantizable representations},
author={Jeong, Yeonwoo and Song, Hyun Oh},
booktitle={arXiv preprint arXiv:1805.05809},
year={2018}
}
- Python3.5
- Deep learning frame work : Tensorflow1.4 gpu Check https://github.com/tensorflow/tensorflow/tree/r1.4
- Ortools(6.6.4656) Check https://developers.google.com/optimization/introduction/download
- Make Directory for data and experiment
cd RROOT
mkdir dataset deep_hash_table_processed deep_hash_table_exp_results
mkdir dataset/Imagenet32
- Change path in config/path.py
RROOT = '(user enter path)'
EXP_PATH = RROOT+'deep_hash_table_exp_results/'
#=============CIFAR100============================#
CIFAR100PATH = RROOT+'dataset/cifar-100-python/'
CIFARPROCESSED = RROOT+'deep_hash_table_processed/cifar_processed/'
#==========================Imagenet32===============================#
IMAGENET32PATH = RROOT+'dataset/Imagenet32/'
IMAGENET32PROCESSED = RROOT+'deep_hash_table_processed/Imagenet32_processed/'
- Download and unzip dataset Cifar-100 and Downsampled imagenet(32x32)
cd RROOT/dataset
wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
tar zxvf cifar-100-python.tar.gz
cd RROOT/dataset/Imagenet32
wget http://www.image-net.org/image/downsample/Imagenet32_train.zip
wget http://www.image-net.org/image/downsample/Imagenet32_val.zip
unzip Imagenet32_train.zip
unzip Imagenet32_val.zip
cd process
python cifar_process.py
python imagenet32_process.py
- Cifar-100 experiment(cifar_exps/) and ImageNet experiment(imagenet_exps/).
- Training metric(metric/)
- train_metric.py is to train embedding with metric learning losses.
- test_metric.py is to test the embedding with the hash codes built with vector quantization method(VQ) and thresholding method(Th).
- Training hash codes(exp1/)
- train_hash.py is to replace the last layer and fine tune the embedding with the proposed method in paper.
- test_hash.py is to test the hash codes built with the embedding trained from train_hash.py.
- Evaluation code is in utils/evaluation.py.
- The hash table built with hash code is evaluated with 3 different metric(NMI, precision@k, SUF).
- The code to solve the dicrete optimization problem in polynomial time is in utils/ortools_op.py
- The time to solve the discrete optimization problem is calculated with the code ortools_exp/
MIT License