mit-han-lab/efficientvit

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3354 stars · 256 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of efficient vision foundation models from MIT Han Lab, including EfficientViT backbones for perception, EfficientViT-SAM for fast segmentation, and DC-AE deep compression autoencoders for high-resolution image generation. It provides pretrained models and training code in Python.

Use cases

• run segment anything models fast on a laptop
• efficient image segmentation backbone for object detection
• compress images for high-resolution diffusion model training
• image generation on imagenet 512x512
• accelerate SAM inference
• efficient vision transformer for edge devices like jetson

When to choose

• you need state-of-the-art efficient SAM segmentation or a fast ViT backbone
• you need a high-compression autoencoder for diffusion-based image generation
• you want pretrained efficient vision models for GPU or edge deployment

When to avoid

• you need active maintenance or future updates - the repo is deprecated in favor of DC-Gen
• you need non-vision machine learning tasks
• you need a no-GPU CPU-only production solution

Facets

library · maturity maintenance

machine-learning image-processing computer-vision llm-inference computer-vision image-processing deep-learning artificial-intelligence python cross-platform vision-transformer segment-anything autoencoder diffusion-models efficient-inference imagenet high-resolution gpu linux

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markdown · JSON · MCP: product_card(name="mit-han-lab/efficientvit")