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README.md

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----
-license: other
-license_name: nvclv1
-license_link: LICENSE
----
+---
+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 introduce a novel mixer block by creating a symmetric path without SSM to enhance the modeling of global context. MambaVision has a hierarchical architecture that employs both self-attention and mixer blocks. 
+
+
+## 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)