---
license: mit
task_categories:
- image-classification
tags:
- robustness
- vssm
- mamba
---

This repository contains the dataset and evaluation artifacts for the paper [Towards Evaluating the Robustness of Visual State Space Models](https://huggingface.co/papers/2406.09407).

Vision State Space Models (VSSMs) are a novel architecture that combines the strengths of recurrent neural networks and latent variable models. This work presents a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios, including:
- **Adversarial attacks**: White-box, Frequency-based, and Transfer-based Black-box attacks.
- **Information Drop**: Occlusions along scanning lines and patch dropping.
- **Natural/Common Corruptions**: Evaluation on ImageNet-C, ImageNet-A, ImageNet-R, ImageNet-S, ImageNet-B, and ImageNet-E.
- **Downstream Tasks**: Robustness on object detection and segmentation (COCO-C, ADE20K-C).

**GitHub Repository:** [HashmatShadab/MambaRobustness](https://github.com/HashmatShadab/MambaRobustness)

## Installation

To set up the environment, follow these steps from the official repository:

```bash
conda create -n mamba_robust
conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r req.txt
cd kernels/selective_scan && pip install .
```

## Sample Usage

### Robustness against Adversarial attacks

To craft adversarial examples using Projected Gradient Descent (PGD) at a perturbation budget of 8/255 with 20 attack steps:

```bash
cd  classification/
python generate_adv_images.py --data_dir <path to dataset> --attack_name pgd  --source_model_name <model_name> --epsilon 8 --attack_steps 20 
```

### Inference on ImageNet Corrupted datasets

For evaluating on ImageNet-A, ImageNet-R, or ImageNet-S:

```bash
cd  classification/
python inference.py --dataset <dataset name> --data_dir <path to corrupted dataset> --batch_size <batch_size> --source_model_name <model name>
```

Supported datasets for the `--dataset` argument include: `imagenet-b`, `imagenet-e`, `imagenet-v2`, `imagenet-a`, `imagenet-r`, and `imagenet-s`.

## Citation

```bibtex
@article{shadab2024towards,
  title={Towards Evaluating the Robustness of Visual State Space Models},
  author={Shadab Malik, Hashmat and Shamshad, Fahad and Naseer, Muzammal and Nandakumar, Karthik and Shahbaz Khan, Fahad and Khan, Salman},
  journal={arXiv e-prints},
  pages={arXiv--2406},
  year={2024}
}
```