EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要