NVlabs/MambaVision

Health v2 · maintenance only

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2224 stars · 149 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MambaVision is NVIDIA's official PyTorch implementation of a hybrid Mamba-Transformer vision backbone, published at CVPR 2025. It provides pretrained models achieving state-of-the-art accuracy-throughput tradeoffs for image classification and downstream tasks like object detection and segmentation.

Use cases

• classify images with a high-throughput pretrained vision backbone
• extract visual features for object detection on COCO
• run semantic segmentation on ADE20K
• fine-tune a hybrid Mamba-Transformer model on my own dataset
• compare Mamba vs Vision Transformer backbones
• use a mamba-based vision model at ImageNet-21K scale

When to choose

• you need a fast, accurate image classification backbone with strong throughput
• you want a hybrid Mamba + self-attention architecture for vision research
• you need pretrained weights for detection or segmentation downstream tasks
• you want to experiment with state-space models in computer vision

When to avoid

• you need a permissively licensed model - it uses the NVIDIA Source Code License-NC (non-commercial)
• you need a lightweight model for CPU-only or edge deployment
• you need a general-purpose NLP or multimodal model rather than a vision backbone

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning image-processing artificial-intelligence python cross-platform vision-backbone mamba vision-transformer pytorch image-classification object-detection semantic-segmentation pretrained-models nvidia cvpr-2025 gpu

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