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--- |
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license: other |
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license_name: nvclv1 |
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license_link: LICENSE |
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datasets: |
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- ILSVRC/imagenet-1k |
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pipeline_tag: image-classification |
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--- |
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[**MambaVision: A Hybrid Mamba-Transformer Vision Backbone**](https://arxiv.org/abs/2407.08083). |
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### Model Overview |
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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. |
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### Model Performance |
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MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in |
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terms of Top-1 accuracy and throughput. |
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<p align="center"> |
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<img src="https://github.com/NVlabs/MambaVision/assets/26806394/79dcf841-3966-4b77-883d-76cd5e1d4320" width=42% height=42% |
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class="center"> |
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</p> |
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### Model Usage |
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You must first login into HuggingFace to pull the model: |
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```Bash |
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huggingface-cli login |
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``` |
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The model can be simply used according to: |
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```Python |
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access_token = "<YOUR ACCESS TOKEN" |
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model = AutoModel.from_pretrained("nvidia/MambaVision-L-1K", trust_remote_code=True) |
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``` |
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### License: |
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[NVIDIA Source Code License-NC](https://huggingface.co/nvidia/MambaVision-L-1K/blob/main/LICENSE) |
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