---
license: mit
pipeline_tag: image-classification
library_name: transformers
tags:
- PyTorch
- Mamba
- SSM
---


# VMamba: Visual State Space Model

VMamba is a bidirectional state-space model finetuned on Imagenet dataset. It was introduced in the paper: 
[VMamba: Visual State Space Model](https://arxiv.org/pdf/2401.10166) and was first released in [this repo](https://github.com/MzeroMiko/VMamba/tree/main).


Disclaimer: This is not the official implementation, please refer to the [official repo](https://github.com/MzeroMiko/VMamba/tree/main).


## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from PIL import Image
import torchvision.transforms as T
from transformers import AutoConfig, AutoModelForImageClassification

config = AutoConfig.from_pretrained('saurabhati/VMamba_ImageNet_82.6',trust_remote_code=True)
vmamba_model = AutoModelForImageClassification.from_pretrained('saurabhati/VMamba_ImageNet_82.6',trust_remote_code=True)

preprocess = T.Compose([
    T.Resize(224, interpolation=Image.BICUBIC),
    T.CenterCrop(224),
    T.ToTensor(),
    T.Normalize(
        mean=[0.4850, 0.4560, 0.4060],
        std=[0.2290, 0.2240, 0.2250]
    )])

input_image = Image.open('/data/sls/scratch/sbhati/data/Imagenet/train/n02009912/n02009912_16160.JPEG')
input_image = preprocess(input_image)

with torch.no_grad():
    logits = vmamba_model(input_image.unsqueeze(0)).logits

predicted_label = vmamba_model.config.id2label[logits.argmax().item()]
predicted_label
'crane'

```

## Citation

```bibtex
@article{liu2024vmamba,
  title={VMamba: Visual State Space Model},
  author={Liu, Yue and Tian, Yunjie and Zhao, Yuzhong and Yu, Hongtian and Xie, Lingxi and Wang, Yaowei and Ye, Qixiang and Liu, Yunfan},
  journal={arXiv preprint arXiv:2401.10166},
  year={2024}
}
```