This is the main repository for our CVPR2020 work:
Auto-Encoding Twin-Bottleneck Hashing

python=3.6
tensorflow>=2.0 (tested with tf2.1)
scipy
sklearn

From this time on, I move to tensorflow2, though it is shitty and inhumane compared with tf1.
Note that this is a re-implemented repository of our original work. If you find any difficulty in reproducing the result, please refer to the old, uncleaned and ugly version.

This work supports tf.data.TFRecordDataset as the data feed. I provide the following data as training examples:

• Cifar-10 (Training, Test)

If one needs to run experiments on other datasets, please refer to util/data/make_data.py to build TFRecords.

Please organize the data folder as follows:

data
  |-cifar10 (or other dataset names)
    |-train.tfrecords
      test.tfrecords

Simply run

python ./run_tbh.py

to train the model.

The resulting checkpoints will be placed in ./result/set_name/model/date_of_today with tensorboard events in /result/set_name/log/date_of_today.

The mAP results shown on tensorboard are just for illustration (the actual score would be slightly higher than the ones on tensorboard), since I do not update all dataset codes upon testing. Please kindly evaluate the results by saving the proceeded codes after training.