Update model card: set pipeline tag, add library name and citation

README.md

@@ -1,13 +1,13 @@
 ---
+datasets:
+- ILSVRC/imagenet-1k
 license: other
 license_name: nvclv1
 license_link: LICENSE
-datasets:
-- ILSVRC/imagenet-1k
-pipeline_tag: image-feature-extraction
+pipeline_tag: image-classification
+library_name: transformers
 ---
 
-
 [**MambaVision: A Hybrid Mamba-Transformer Vision Backbone**](https://arxiv.org/abs/2407.08083).
 
 ## Model Overview
@@ -17,38 +17,34 @@ We have developed the first hybrid model for computer vision which leverages the
 ## Model Performance
 
 MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in
-terms of Top-1 accuracy and throughput. 
+terms of Top-1 accuracy and throughput.
 
 <p align="center">
-<img src="https://github.com/NVlabs/MambaVision/assets/26806394/79dcf841-3966-4b77-883d-76cd5e1d4320" width=70% height=70% 
+<img src="https://github.com/NVlabs/MambaVision/assets/26806394/79dcf841-3966-4b77-883d-76cd5e1d4320" width=70% height=70%
 class="center">
 </p>
 
-
 ## Model Usage
 
 It is highly recommended to install the requirements for MambaVision by running the following:
 
-
 ```Bash
 pip install mambavision
 ```
 
-For each model, we offer two variants for image classification and feature extraction that can be imported with 1 line of code. 
+For each model, we offer two variants for image classification and feature extraction that can be imported with 1 line of code.
 
 ### Image Classification
 
-In the following example, we demonstrate how MambaVision can be used for image classification. 
-
-Given the following image from [COCO dataset](https://cocodataset.org/#home)  val set as an input:
+In the following example, we demonstrate how MambaVision can be used for image classification.
 
+Given the following image from [COCO dataset](https://cocodataset.org/#home) val set as an input:
 
 <p align="center">
-<img src="https://cdn-uploads.huggingface.co/production/uploads/64414b62603214724ebd2636/4duSnqLf4lrNiAHczSmAN.jpeg" width=70% height=70% 
+<img src="https://cdn-uploads.huggingface.co/production/uploads/64414b62603214724ebd2636/4duSnqLf4lrNiAHczSmAN.jpeg" width=70% height=70%
 class="center">
 </p>
 
-
 The following snippet can be used for image classification:
 
 ```Python
@@ -77,18 +73,18 @@ transform = create_transform(input_size=input_resolution,
 inputs = transform(image).unsqueeze(0).cuda()
 # model inference
 outputs = model(inputs)
-logits = outputs['logits'] 
+logits = outputs['logits']
 predicted_class_idx = logits.argmax(-1).item()
 print("Predicted class:", model.config.id2label[predicted_class_idx])
 ```
 
-The predicted label is ```brown bear, bruin, Ursus arctos.```
+The predicted label is `brown bear, bruin, Ursus arctos.`
 
 ### Feature Extraction
 
-MambaVision can also be used as a generic feature extractor. 
+MambaVision can also be used as a generic feature extractor.
 
-Specifically, we can extract the outputs of each stage of model (4 stages) as well as the final averaged-pool features that are flattened. 
+Specifically, we can extract the outputs of each stage of model (4 stages) as well as the final averaged-pool features that are flattened.
 
 The following snippet can be used for feature extraction:
 
@@ -98,7 +94,7 @@ from PIL import Image
 from timm.data.transforms_factory import create_transform
 import requests
 
-model = AutoModel.from_pretrained("nvidia/MambaVision-T2-1K", trust_remote_code=True)
+model = AutoModel.from_pretrained("nvidia/MambaVision-T-1K", trust_remote_code=True)
 
 # eval mode for inference
 model.cuda().eval()
@@ -123,7 +119,41 @@ print("Size of extracted features in stage 1:", features[0].size()) # torch.Size
 print("Size of extracted features in stage 4:", features[3].size()) # torch.Size([1, 640, 7, 7])
 ```
 
+### License:
+
+[NVIDIA Source Code License-NC](https://huggingface.co/nvidia/MambaVision-T-1K/blob/main/LICENSE)
+
+## Citation
+
+If you find MambaVision to be useful for your work, please consider citing our paper:
+
+```
+@article{hatamizadeh2024mambavision,
+  title={MambaVision: A Hybrid Mamba-Transformer Vision Backbone},
+  author={Hatamizadeh, Ali and Kautz, Jan},
+  journal={arXiv preprint arXiv:2407.08083},
+  year={2024}
+}
+```
+
+## Star History
+
+[![Stargazers repo roster for @NVlabs/MambaVision](https://bytecrank.com/nastyox/reporoster/php/stargazersSVG.php?user=NVlabs&repo=MambaVision)](https://github.com/NVlabs/MambaVision/stargazers)
+
+[![Star History Chart](https://api.star-history.com/svg?repos=NVlabs/MambaVision&type=Date)](https://star-history.com/#NVlabs/MambaVision&Date)
+
+## Licenses
+
+Copyright © 2025, NVIDIA Corporation. All rights reserved.
+
+This work is made available under the NVIDIA Source Code License-NC. Click [here](LICENSE) to view a copy of this license.
+
+The pre-trained models are shared under [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+For license information regarding the timm repository, please refer to its [repository](https://github.com/rwightman/pytorch-image-models).
+
+For license information regarding the ImageNet dataset, please see the [ImageNet official website](https://www.image-net.org/).
 
-### License: 
+## Acknowledgement
 
-[NVIDIA Source Code License-NC](https://huggingface.co/nvidia/MambaVision-T-1K/blob/main/LICENSE)
+This repository is built on top of the [timm](https://github.com/huggingface/pytorch-image-models) repository. We thank [Ross Wrightman](https://rwightman.com/) for creating and maintaining this high-quality library.