This is an official code implementation repository for Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation, accepted to ECCV 2024.
- We use Python 3.6 and Pytorch 1.9.0
- The codebase from Detectron2.
git clone https://github.com/junia3/LPLD.git
conda create -n LPLD python=3.6
conda activate LPLD
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
cd LPLD
pip install -r requirements.txt
## Make sure you have GCC and G++ version <=8.0
cd ..
python -m pip install -e LPLD
- Cityscapes, FoggyCityscapes / Download Webpage
- PASCAL_VOC / Download Webpage
- Clipart / Download Webpage
- Watercolor / Download Webpage
- Sim10k / Download Webpage
Make sure that all downloaded datasets are located in the ./dataset folder. After preparing the datasets, you will have the following file structure:
LPLD
...
├── dataset
│ └── foggy
│ └── cityscape
│ └── clipart
│ └── watercolor
...Make sure that all dataset fit the format of PASCAL_VOC. For example, the dataset foggy is stored as follows:
$ cd ./dataset/foggy/VOC2007/
$ ls
Annotations ImageSets JPEGImages
$ cat ImageSets/Main/test_t.txt
target_munster_000157_000019_leftImg8bit_foggy_beta_0.02
target_munster_000124_000019_leftImg8bit_foggy_beta_0.02
target_munster_000110_000019_leftImg8bit_foggy_beta_0.02
.
.@inproceedings{yoon2024enhancing,
title={Enhancing Source-Free Domain Adaptive Object Detection with Low-Confidence Pseudo Label Distillation},
author={Yoon, Ilhoon and Kwon, Hyeongjun and Kim, Jin and Park, Junyoung and Jang, Hyunsung and Sohn, Kwanghoon},
booktitle={European Conference on Computer Vision},
pages={337--353},
year={2024},
organization={Springer}
}