This repository contains codes and models for the following paper:

Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, and Ying-Cong Chen. MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders. In European Conference on Computer Vision, 2024.

• PyTorch 2.0.0

• timm 0.9.16

• mmsegmentation 1.2.2

• mamba-ssm 1.1.2

• CUDA 11.8

1. Prepare the pretrained Swin-Large checkpoint by running the following command

   cd pretrained_ckpts
   bash run.sh
   cd ../

2. Download the data from PASCALContext.tar.gz, NYUDv2.tar.gz, and then extract them. You need to modify the dataset directory as db_root variable in configs/mypath.py.

3. Train the model. Taking training NYUDv2 as an example, you can run the following command

   python -m torch.distributed.launch --nproc_per_node 8 main.py --run_mode train --config_exp ./configs/mtmamba_nyud.yml 

        You can download the pretrained models from mtmamba_nyud.pth.tar, mtmamba_pascal.pth.tar.

4. Evaluation. You can run the following command,

   python -m torch.distributed.launch --nproc_per_node 1 main.py --run_mode infer --config_exp ./configs/mtmamba_nyud.yml --trained_model ./ckpts/mtmamba_nyud.pth.tar

We would like to thank the authors that release the public repositories: Multi-Task-Transformer, mamba, and VMamba.

If you found this code/work to be useful in your own research, please cite the following:

@inproceedings{lin2024mtmamba,
  title={{MTM}amba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders},
  author={Lin, Baijiong and Jiang, Weisen and Chen, Pengguang and Zhang, Yu and Liu, Shu and Chen, Ying-Cong},
  booktitle={European Conference on Computer Vision},
  year={2024}
}