ParetoDA

This repo provides a demo for the NIPS 2021 paper "Pareto Domain Adaptation" on the VisDA-2017 dataset. [Paper]

Requirements

• Python 3.6
• Pytorch 1.1.0

Training from scratch

Please first download the VisDA-2017 dataset from https://github.com/VisionLearningGroup/taskcv-2017-public. Then update the train and validation files with suffix ".txt" following styles below:

data/visda2017/train/aeroplane/aeroplane_src_001.jpg 0
...

data/visda2017/validation/aeroplane/aeroplane_001.jpg 0
...

Then train on VisDA2017 with ResNet101:

python DANN+ParetoDA.py --gpu_id 0 --arch resnet101 --train_path xxx --val_path xxx

Acknowledgements

Some codes are adapted from EPOSearch. We thank them for their excellent projects.

Contact

If you have any problem about our code, feel free to contact [email protected] or describe your problem in Issues.