EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

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...

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Bibliographic Details
Main Authors: Zhang, Zhuoyang, Cai, Han, Han, Song
Format: Conference Proceeding
Language:English
Subjects:
Codes
Computational modeling
Computer vision
Conferences
Graphics processing units
Image segmentation
Training
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Summary: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 48.9× 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.
ISSN:2160-7516
DOI:10.1109/CVPRW63382.2024.00782