TF similarity benchmarking but with TFRecordDatasetSampler.
- Run
save_tfrecords_classes.py
to generate training, testing, seen queries, unseen queries, and index partitions. Each folder has tfrecords separated by class - Run
train.py
to train and save model - Run
evaluate.py
to get evaluation metrics
Problems - cannot see binary accuracy while training due to RAM limits
When evaluating cannot sample from unseen queries, seen queries, or index datasets because of not getting enough classes? Have just used some code from https://github.com/tensorflow/similarity/blob/kaggle/examples/supervised/kaggle_keras_tuner.ipynb which does this sampling. Cannot use all of test dataset due to ram limits as well.