EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Zhuoyang Zhang, Han Cai, Song Han

EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss: 24 upvotes on Hugging Face Daily Papers, #7 of 14 papers on 2024-02-08. Day-by-day upvote history.

We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM's lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT's efficiency and capacity, EfficientViT-SAM delivers 48.9x measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://github.com/mit-han-lab/efficientvit.

Paper page on Hugging Face · arXiv

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