Paper · arXiv 2402.05008
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Zhuoyang Zhang, Han Cai, Song Han
EfficientViT-SAM combines SAM's prompt encoder and mask decoder with EfficientViT as the image encoder, achieving significant speedup without losing performance.
Abstract
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.