EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss
<|reference_start|>EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy 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 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.<|reference_end|>
@article{zhang2024efficientvit-sam:,
    title={EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy
  Loss},
    author={Zhuoyang Zhang, Han Cai, Song Han},
    journal={arXiv preprint arXiv:2402.05008},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05008},
    primaryClass={cs.CV cs.AI cs.LG}
}