EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
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.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM (2023)
- TinySAM: Pushing the Envelope for Efficient Segment Anything Model (2023)
- CAT-SAM: Conditional Tuning Network for Few-Shot Adaptation of Segmentation Anything Model (2024)
- PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation (2024)
- MobileSAMv2: Faster Segment Anything to Everything (2023)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 1
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper