EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss

TL;DR

EfficientViT-SAM introduces a faster segment anything model by replacing the heavy image encoder with EfficientViT, achieving significant speedup without losing accuracy, enabling more efficient image segmentation tasks.

Contribution

This work develops EfficientViT-SAM, combining EfficientViT with SAM's architecture, and demonstrates substantial speed improvements through knowledge distillation and end-to-end training.

Findings

48.9x speedup on A100 GPU

Maintains original segmentation performance

Open-source code and models provided

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.