Our code is based on
please install them before usage.
Compared with previous cross-domain object detection methods, D-adapt decouples the adversarial adaptation from the training of detector.
The whole pipeline is as follows:
First, you need to run source_only.py to obtain pre-trained models. (See source_only.sh for scripts.)
Then you need to run d_adapt.py to obtain adapted models. (See d_adapt.sh for scripts).
When the domain discrepancy is large, you need to run d_adapt.py multiple times.
For better readability, we implement the training of category adaptor in category_adaptation.py,
implement the training of the bounding box adaptor inbbox_adaptation.py,
and implement the training of the detector and connect the above components in d_adapt.py.
This can facilitate you to modify and replace other adaptors.
We provide independent training arguments for detector, category adaptor and bounding box adaptor.
The arguments of latter two end with -c and -b respectively.
If you use these methods in your research, please consider citing.
@inproceedings{jiang2021decoupled,
title = {Decoupled Adaptation for Cross-Domain Object Detection},
author = {Junguang Jiang and Baixu Chen and Jianmin Wang and Mingsheng Long},
booktitle = {ICLR},
year = {2022}
}