Sampling-Free Universal Domain Adaptive Object Detection with Synergistic Optimization Alignment

Requirements

• Python >= 3.8
• PyTorch >= 2.1.0+cu118
• torchvision >= 0.16.0+cu118
• CUDA 11.8
• Linux

Install dependencies:

pip install torch==2.1.0+cu118 torchvision==0.16.0+cu118 \
    --index-url https://download.pytorch.org/whl/cu118

pip install ninja yacs cython matplotlib tqdm opencv-python pycocotools

Check CUDA:

python -c "import torch; print(torch.__version__, torch.cuda.is_available())"

Build

python setup.py build develop

Dataset

Dataset paths are configured in:

maskrcnn_benchmark/config/paths_catalog.py

Modify the entries in DatasetCatalog.DATASETS, for example:

"openset_voc07_0.5": {
    "data_dir": "root/datasets/VOCdevkit/VOC2007/",
    "split": "trainval"
},

Training

sh train_script/train_voc05.sh > voc05.out 
sh train_script/train_voc2water.sh > voc2water.out 
sh train_script/train_water2voc.sh > water2voc.out 
sh train_script/train_voc075.sh > voc075.out 
sh train_script/train_voc025.sh > voc025.out
sh train_script/train_city2foggy.sh > foggy.out

Testing

python tools/test_net.py \
    --config-file configs/UniDAOD/da_rcnn_fpn_voc05.yaml \
    MODEL.WEIGHT output_dic/baseline/voc05/model_final.pkl

📫 Contact

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