Prototypical Cross Domain Self-Supervised Learning for Few-shot Unsupervised Domain Adaptation in Semantic Segmentation

Pytorch implementation of PCS (Prototypical Cross-domain Self-supervised network) [link to our report]

Overview

Architecture of Network

Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 4.3%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively. q

Requirements

conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
pip install -e .

Training

• Download or soft-link your dataset under data folder (Split files are provided in data/splits, supported datasets are Office, Office-Home, VisDA-2017, and DomainNet)
• To train the model, run following commands:

CUDA_VISIBLE_DEVICES=0 python pcs/run.py --config config/${DATASET}/${DOMAIN-PAIR}.json
CUDA_VISIBLE_DEVICES=0,1 python pcs/run.py --config config/office/D-A-1.json