# Caffe-DeepBinaryCode

**Repository Path**: saturos/Caffe-DeepBinaryCode

## Basic Information

- **Project Name**: Caffe-DeepBinaryCode
- **Description**: :star: Supervised Semantics-preserving Deep Hashing (SSDH)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2015-09-30
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

_FYI: **We will provide the training scripts and user guide soon!!!** If you'd like to run the training code now, please make sure you already have **the dataset (leveldb or lmdb, w/h=256/256)** and **ImageNet pre-trained weights**_

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# Caffe-DeepBinaryCode

Implementation of the Supervised Semantics-preserving Deep Hashing (SSDH)

Created by Huei-Fang Yang, Kevin Lin, and Chu-Song Chen at Academia Sinica, Taipei, Taiwan.

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### Introduction

We present a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. SSDH constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. Compared to state-of-the-art results, SSDH achieves 26.30% (89.68% vs. 63.38%), 17.11% (89.00% vs. 71.89%) and 19.56% (31.28% vs. 11.72%) higher precisions averaged over a different number of top returned images for the CIFAR-10, NUS-WIDE, and SUN397 datasets, respectively.

This modified caffe distribution provides our objective function to learn efficient binary hash codes. 

The details can be found in the following [arXiv preprint.](http://arxiv.org/abs/1507.00101)

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### Citing the deep hashing work

If you find our work useful in your research, please consider citing:

    Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search
    Huei-Fang Yang, Kevin Lin, Chu-Song Chen
    arXiv preprint arXiv:1507.00101