We present a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. The supervised semantics-preserving deep hashing (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. This is the extended version of our "CVPRW'15 paper." The details can be found in the following "TPAMI'17 paper":

Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks
Huei-Fang Yang, Kevin Lin, Chu-Song Chen
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017

please cite the paper if you use the model:
* Caffe-DeepBinaryCode: See our code release on Github, which allows you to train your own deep hashing model and create binary hash codes.

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