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

Feel free to contribute code.

Refactor this project.

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

Deep hash for "A", "B".

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

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.

(削除) ## Test (削除ここまで)

(削除) 1. Create folder test, and create pos, neg in test with pictures that you want to retrive. (削除ここまで)

(削除) 2. Run testQue.py to query your picture set. (削除ここまで)