Code for the paper:
Mert Keser, Artem Savkin, Federico Tombari, “Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation”, IEEE IROS 2021
Dockerfile:
FROM nvidia/cuda:9.2-cudnn7-runtime-ubuntu18.04
RUN apt-get update
RUN apt-get update && apt-get install -y python3-dev python3-pip
RUN pip3 install --upgrade pip
RUN pip3 install torch==1.3.1
RUN pip3 install torchvision==0.4.2
Train command:
python secogan/train.py \
--name=<experiment_name> \
--gpu_ids=0 \
--data_source=<source_data_path> \
--data_target=<target_data_path> \
--output_dir=<output_path> \
--batch_size=4
If you find this code useful for your research, please cite our paper:
@inproceedings{Keser2021,
title={Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation},
author={Keser, Mert and Savkin, Artem and Tombari, Federico},
booktitle={IEEE IROS},
year={2021}
}