This is the main repository for our CVPR2020 work:
which is an unsupervised deep hashing work.
We as well provide the code of our supervised deep hashing work on ICCV2019 Workshops here:
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:
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.
Simply run
python ./run_jmlh.py
to train the model.
The resulting checkpoints will be placed in ./result/set_name_JMLH/model/date_of_today with tensorboard events in ./result/set_name_JMLH/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.