Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment (DPA)

Code implementation for Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment (DPA)

Requirements

• Ubuntu 18.04.5 LTS
• Python 3.6
• CUDA 10.0
• PyTorch 1.0.0
• Faster R-CNN

Compile the code

#Compile the cuda dependencies using following simple commands following [Faster R-CNN](https://github.com/jwyang/faster-rcnn.pytorch/tree/pytorch-1.0):
cd lib
python setup.py build develop

Pre-trained Models

• ResNet101: Dropbox VT Server

Components

Global-level Domain Private Alignment (GDPA)

• Path: UniDAOD-DPA/lib/model/utils
• Function: global_alignment()

Instance-level Domain Shared Alignment (IDSA)

• Path: UniDAOD-DPA/lib/model/da_faster_rcnn/
• File: openset_weight.py

Private Class Constraint (PCC)

• Path: UniDAOD-DPA/lib/model/utils
• Function: instance_alignment_private

Training and Test

Train the model

CUDA_VISIBLE_DEVICES=0 python -u da_train_net.py \
--max_epochs 10 --cuda --dataset voc2clipart_0.25 \
--net res101 --save_dir ./weight_model/voc2clipart_0.25 \
--pretrained_path XXXX/pretrained_model/resnet101_caffe.pth \
--gc --lc --da_use_contex --weight_consis 0.1 --lr_bound 0.1 --gmm_split 0.03

Test the well-trained model:

python test_clipart_0.25.py >> test-voc025.out

Train the model and test the well-trained model through the script:

sh train_scripts\train_voc2clipart_0.25.sh

If you have any questions , please contact me at [email protected]

@article{zheng2024universal,
  title={Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment},
  author={Zheng, Yuanfan and Wu, Jinlin and Li, Wuyang and Chen, Zhen},
  journal={arXiv preprint arXiv:2412.11443},
  year={2024}
}