nvidia
/

MambaVision-B-1K

@@ -1,129 +1,26 @@
 ---
-license: other
-license_name: nvclv1
-license_link: LICENSE
-datasets:
-- ILSVRC/imagenet-1k
-pipeline_tag: image-feature-extraction
 ---
-[**MambaVision: A Hybrid Mamba-Transformer Vision Backbone**](https://arxiv.org/abs/2407.08083).
-## Model Overview
-We have developed the first hybrid model for computer vision which leverages the strengths of Mamba and Transformers. Specifically, our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. In addition, we conducted a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba. Our results demonstrate that equipping the Mamba architecture with several self-attention blocks at the final layers greatly improves the modeling capacity to capture long-range spatial dependencies. Based on our findings, we introduce a family of MambaVision models with a hierarchical architecture to meet various design criteria.
-## Model Performance
-MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in
-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%
-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.
-### 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:
-<p align="center">
-<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
-from transformers import AutoModelForImageClassification
-from PIL import Image
-from timm.data.transforms_factory import create_transform
-import requests
-model = AutoModelForImageClassification.from_pretrained("nvidia/MambaVision-B-1K", trust_remote_code=True)
-# eval mode for inference
-model.cuda().eval()
-# prepare image for the model
-url = 'http://images.cocodataset.org/val2017/000000020247.jpg'
-image = Image.open(requests.get(url, stream=True).raw)
-input_resolution = (3, 224, 224)  # MambaVision supports any input resolutions
-transform = create_transform(input_size=input_resolution,
-                             is_training=False,
-                             mean=model.config.mean,
-                             std=model.config.std,
-                             crop_mode=model.config.crop_mode,
-                             crop_pct=model.config.crop_pct)
-inputs = transform(image).unsqueeze(0).cuda()
-# model inference
-outputs = model(inputs)
-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.```
-### Feature Extraction
-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.
-The following snippet can be used for feature extraction:
-```Python
-from transformers import AutoModel
-from PIL import Image
-from timm.data.transforms_factory import create_transform
-import requests
-model = AutoModel.from_pretrained("nvidia/MambaVision-B-1K", trust_remote_code=True)
-# eval mode for inference
-model.cuda().eval()
-# prepare image for the model
-url = 'http://images.cocodataset.org/val2017/000000020247.jpg'
-image = Image.open(requests.get(url, stream=True).raw)
-input_resolution = (3, 224, 224)  # MambaVision supports any input resolutions
-transform = create_transform(input_size=input_resolution,
-                             is_training=False,
-                             mean=model.config.mean,
-                             std=model.config.std,
-                             crop_mode=model.config.crop_mode,
-                             crop_pct=model.config.crop_pct)
-inputs = transform(image).unsqueeze(0).cuda()
-# model inference
-out_avg_pool, features = model(inputs)
-print("Size of the averaged pool features:", out_avg_pool.size())  # torch.Size([1, 640])
-print("Number of stages in extracted features:", len(features)) # 4 stages
-print("Size of extracted features in stage 1:", features[0].size()) # torch.Size([1, 80, 56, 56])
-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)

 ---
+license: apache-2.0
+task_categories:
+- video-text-to-text
 ---
+This repository contains the data for the paper [PAVE: Patching and Adapting Video Large Language Models](https://arxiv.org/abs/2503.19794).
+Code: https://github.com/dragonlzm/PAVE
+## Citation [optional]
+arxiv.org/abs/2503.19794
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
 ```
+@misc{liu2025pavepatchingadaptingvideo,
+      title={PAVE: Patching and Adapting Video Large Language Models},
+      author={Zhuoming Liu and Yiquan Li and Khoi Duc Nguyen and Yiwu Zhong and Yin Li},
+      year={2025},
+      eprint={2503.19794},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.19794},
+}
+```