TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning

This is the official implementation of the paper: TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning based on the MTLoRA

Run TADFormer

1. Clone the repository

   git clone git@github.com:Min100KM/TADFormer.git
   cd TADFormer

2. Install requirements

   • Install PyTorch>=1.12.0 and torchvision>=0.13.0 with CUDA>=11.6
   • Install dependencies: pip install -r requirements.txt

3. Running TADFormer code:

Run the code

python -m torch.distributed.launch --nproc_per_node 1 --master_port=12345 \
main.py --cfg configs/TADFormer/[config_name].yaml \
--pascal [pascal_dataset] --tasks semseg,normals,sal,human_parts \
--batch-size 32 --ckpt-freq=20 --epoch=300 --resume-backbone [Pretrained Swin Transformer .pth path] \
--disable_wandb

Eval

python -m torch.distributed.launch --nproc_per_node 1 --master_port=12345 \
main.py --cfg configs/TADFormer/[config_name].yaml \
--pascal [pascal_dataset] --tasks semseg,normals,sal,human_parts \   
--batch-size 32 --ckpt-freq=20 --epoch=300 --resume [.pth path] \
--eval \
--disable_wandb

Citation

@InProceedings{Baek_2025_CVPR,
    author    = {Baek, Seungmin and Lee, Soyul and Jo, Hayeon and Choi, Hyesong and Min, Dongbo},
    title     = {TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14858-14868}
}

Acknowlegment

This repo benefits from the MTLoRA and ddfnet.