EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss

Han Cai; Song Han; Zhuoyang Zhang

EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss

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arxiv 2402.05008 v2 pith:GLU4F4NX submitted 2024-02-07 cs.CV cs.AIcs.LG

EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss

Zhuoyang Zhang , Han Cai , Song Han This is my paper

classification cs.CV cs.AIcs.LG

keywords efficientvitefficientvit-samencoderacceleratedanythingimagemodelssam-vit-h

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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.

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