Real-time domain adaptation for semantic segmentation

Overview

Advanced-Machine-Learning

This repository contains the code for the project Real-time domain adaptation for semantic segmentation, relative to the course Advanced Machine Learning.

Goals

  • The first goal of the project is to implement and test BiSeNet, a deep network for semantic segmentation, on Cityscapes. The description of the network is in the folder model, while the file to train it on the labeled dataset is train.py.
  • Secondly, the projects aims at training the network on a domain-adaptation task. In particular, the network is trained using the labeled GTA5 dataset as source domain and the unlabeled Cityscapes as target domain. A discriminator network to distinguish between the two domains and help in learning meaningful representations is described in model/discriminator.py, whereas the file to perform the training is newtrain.py.
  • In conclusion, the performances of domain adaptation are improved by implementing a pseudo labeling technique. In particular, pseudo labels are generated for the target domain (Cityscapes) and are used for training in the next iteration. The file to perform the training is pseudo_labels_train.py, whereas the file to generate pseudo labels is SSL.py.
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