Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography

This repository contains the code used to run the active learning experiments detailed in our paper.

Dependencies

In order to run our code, you'll need these main packages:

The code is run using the main.py file in the following way:

python main.py --lambda_e xxx --classes xxx --batch_size xxx --initial_size xxx --iterations xxx --gpu xxx

lambda_e: a hyperparameter that balances entropy and discriminative scores.
classes: the number of classes for classification.
batch_size: the size of the batch of examples to be labeled in every iteration.
initial_size: the amount of labeled examples to start the experiment with (chosen randomly).
iterations: the amount of active learning iterations to run in the experiment.