lix-byte / Triplet-deep-hash-pytorch

Pytorch implementation of "Fast Training of Triplet-based Deep Binary Embedding Networks".

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Triplet-deep-hash-pytorch

Pytorch implementation of "Fast Training of Triplet-based Deep Binary Embedding Networks". http://arxiv.org/abs/1603.02844

Feel free to contribute code.

Update 2017.11.13

Refactor this project.

Use code in https://github.com/kentsommer/keras-inceptionV4 to extract feature.

DEMO

Deep hash for "A", "B".

TODO

  • Add multiclass support.
  • Make code clean.
  • Add more base networks.
  • Add query code for new project.

Usage

Train

  1. Put training pictures in train/[category-id], test pictures in data/test.
  2. Run src/extract_feature/batch_extarct_test.py and src/extract_feature/batch_extract_train.py to extract feature for future use.
  3. Run src/hash_net/generate_random_dataset.py to generate random training data.
  4. Run src/hash_net/hashNet.py to train your triplet deep hash network.

## Test

1. Create folder test, and create pos, neg in test with pictures that you want to retrive.

2. Run testQue.py to query your picture set.

About

Pytorch implementation of "Fast Training of Triplet-based Deep Binary Embedding Networks".

License:Apache License 2.0


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