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Mean Teacher Image Segmentation

This is an implementation developed for the semi-supervised semantic segmentation task of the Oxford IIIT Pet dataset. This implementation is based on the work of The Curious AI Company and their publication: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results (1703.01780).

TODO:

  • Increase image size to 256px - 64px not enough but not enought GPU memory (AWS?)

Package requirements:

Files:

  • main.py: main script running creating the dataset, student and teacher models, optimizer, and running the training and evaluation. Saves a pickle file of the training and evaluation metrics as well as the two models.
  • dataset.py: Custom LabeledUnlabeledPetDataset class to store sample and its associated labels, apply transforms as well as creating a the labelled unlabelled split of our data.
  • data.py: Data loading utility functions, including the two stream sampler for the dataloader.
  • ramp_up.py: Functions associated with the ramping up of the weights during training.
  • utils.py: Utility functions.

To run the training and eval simply run:

foo@bar:~/MeanTeacher$ python main.py