Add dataset card, link to paper and GitHub repository

README.md

@@ -1,3 +1,64 @@
 ---
 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}
+}
+```