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:heart::coffee: Deep Learning of Binary Hash Codes for Fast Image Retrieval (CVPRW15)
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# Caffe-cvprw15
Deep Learning of Binary Hash Codes for Fast Image Retrieval
Created by Kevin Lin, Huei-Fang Yang, and Chu-Song Chen at Academia Sinica, Taipei, Taiwan.
## Introduction
We present a simple yet effective deep learning framework to create the hash-like binary codes for fast image retrieval. We add a latent-attribute layer in the deep CNN to simultaneously learn domain specific image representations and a set of hash-like functions. Our method does not rely on pairwised similarities of data and is highly scalable to the dataset size. Experimental results show that, with only a simple modification of the deep CNN, our method improves the previous best retrieval results with 1% and 30% retrieval precision on the MNIST and CIFAR-10 datasets, respectively. We further demonstrate the scalability and efficacy of the proposed approach on the large-scale dataset of 1 million shopping images.
The details can be found in the following [CVPRW 2015 paper](http://www.iis.sinica.edu.tw/~kevinlin311.tw/cvprw15.pdf)
## Citing the deep hashing works
If you find our works useful in your research, please consider citing:
Deep Learning of Binary Hash Codes for Fast Image Retrieval
K. Lin, H.-F. Yang, J.-H. Hsiao, C.-S. Chen
CVPR Workshop (CVPRW) on Deep Learning in Computer Vision, DeepVision 2015, June 2015.
Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search
K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, C.-S. Chen
ACM International Conference on Multimedia Retrieval, ICMR 2015, June 2015.
## CIFAR10 retrieval results
Performance comparison of different hashing methods on CIFAR10 dataset. The table shows the mean average precision (mAP) of top 1000 returned images with respect to different number of hash bits.
| Method | 12 bits | 32 bits | 48 bits
|----------------|:-----------:|:-----------:|:-----------:
| Ours | 89.30 | 89.72 | 89.73
| CNNH+ | 46.50 | 52.10 | 53.20
| CNNH | 43.90 | 50.90 | 52.20
| KSH | 30.30 | 34.60 | 35.60
| ITQ-CCA | 26.40 | 28.80 | 29.50
| LSH | 12.10 | 12.00 | 12.00
## Prerequisites
0. MATLAB (tested with 2012b on 64-bit Linux)
0. Caffe's [prerequisites](http://caffe.berkeleyvision.org/installation.html#prequequisites)
## Install Caffe-cvprw15
Adjust Makefile.config and simply run the following commands:
$ make all -j8
$ make test -j8
$ make runtest -j8
$ make matcaffe
$ ./download_model.sh
For a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
## Demo
This demo generates 48-bits binary codes using our model trained on CIFAR10.
Launch matlab and run `demo.m`
>> demo
## Retrieval evaluation on CIFAR10
First, run script `prepare_eval.sh` to download and setup CIFAR10 dataset.
$ ./prepare_eval.sh
Second, launch matalb and run `run_cifar10.m` to perform the evaluation of `precision at k` and `mean average precision at k`. We set `k=1000` in the experiments. The bit length of binary codes is `48`. This process takes around 12 minutes.
>> run_cifar10
Then, you will get the `mAP` result as follows.
>> MAP = 0.897373
Moreover, simply run the following commands to generate the `precision at k` curves:
$ cd analysis
$ gnuplot plot-p-at-k.gnuplot
You will reproduce the precision curves with respect to different number of top retrieved samples when the 48-bit hash codes are
used in the evaluation:
<img src="https://www.csie.ntu.edu.tw/~r01944012/cvprw15-cifar10.png" width="500">
## Train your own model on CIFAR10
First, run script `prepare_train.sh` to download ImageNet pretrained model and convert CIFAR10 dataset to leveldb foExcerpt of 9,103 characters
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Kevin Ke-Yun Lin · Microsoft; University of Washington · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:caa334d473520a5e, llm:Repository title and description: 'Deep Learning of Binary Hash Codes for Fast Image Retrieval (CVPRW15)'. Language: C++. Indicates implementation of deep learning method for binary hash codes for image retrieval from CVPR workshop paper.
matched fp:caa334d473520a5e, llm:Repository title and description: 'Deep Learning of Binary Hash Codes for Fast Image Retrieval (CVPRW15)'. Language: C++. Indicates implementation of deep learning method for binary hash codes for image retrieval from CVPR workshop paper.
matched fp:caa334d473520a5e, llm:Repository title and description: 'Deep Learning of Binary Hash Codes for Fast Image Retrieval (CVPRW15)'. Language: C++. Indicates implementation of deep learning method for binary hash codes for image retrieval from CVPR workshop paper.