Reinforced Active Learning for Image Segmentation (RALIS)

Code for the paper Reinforced Active Learning for Image Segmentation

Dependencies

• python 3.6.5
• numpy 1.14.5
• scipy 1.1.0
• Pytorch 0.4.0

Scripts

The folder 'scripts' contains the different bash scripts that could be used to train the same models used in the paper, for both Camvid and Cityscapes datasets.

• launch_supervised.sh: To train the pretrained segmentation models.
• launch_baseline.sh: To train the baselines 'random', 'entropy' and 'bald'.
• launch_train_ralis.sh: To train the 'ralis' model.
• launch_test_ralis.sh: To test the 'ralis' model.

Datasets

Camvid: https://github.com/alexgkendall/SegNet-Tutorial/tree/master/CamVid

Cityscapes: https://www.cityscapes-dataset.com/

Trained models

To download the trained RALIS models for Camvid and Cityscapes (as well as the pretrained segmentation model on GTA and D_T subsets): https://drive.google.com/file/d/13C4e0bWw6SEjTAD7JdAfLGVz7p7Veeb9/view?usp=sharing

Citation

If you use this code, please cite:

@inproceedings{
Casanova2020Reinforced,
title={Reinforced active learning for image segmentation},
author={Arantxa Casanova and Pedro O. Pinheiro and Negar Rostamzadeh and Christopher J. Pal},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SkgC6TNFvr}
}