README.md

✨ Pre-trained Models

📚 Data Preparation

• ImageNet is an image database organized according to the WordNet hierarchy. Download and extract ImageNet train and val images from http://image-net.org/. Organize the data into the following directory structure:

  imagenet/
  ├── train/
  │   ├── n01440764/  (Example synset ID)
  │   │   ├── image1.JPEG
  │   │   ├── image2.JPEG
  │   │   └── ...
  │   ├── n01443537/  (Another synset ID)
  │   │   └── ...
  │   └── ...
  └── val/
      ├── n01440764/  (Example synset ID)
      │   ├── image1.JPEG
      │   └── ...
      └── ...
  

• COCO is a large-scale object detection, segmentation, and captioning dataset. Please visit http://cocodataset.org/ for more information, including for the data, paper, and tutorials. COCO API also provides a concise and efficient way to process the data.

• ADE20K is composed of more than 27K images from the SUN and Places databases. Please visit https://ade20k.csail.mit.edu/ for more information and see the GitHub Repository for an overview of how to access and explore ADE20K.

🖊️ Citation

@article{li2025damamba,
  title={DAMamba: Vision State Space Model with Dynamic Adaptive Scan},
  author={Li, Tanzhe and Li, Caoshuo and Lyu, Jiayi and Pei, Hongjuan and Zhang, Baochang and Jin, Taisong and Ji, Rongrong},
  journal={arXiv preprint arXiv:2502.12627},
  year={2025}
}

💌 Acknowledgments

This project is largely based on Mamba, VMamba, Swin-Transformer, InternImage and OpenMMLab. We are truly grateful for their excellent work.

🎫 License

This project is released under the Apache 2.0 license.