Researchers from S-Lab NTU and Shanghai AI Lab developed EdgeSAM, an optimized variant of SAM for real-time object segmentation on edge devices. It outperforms Mobile-SAM by 14x and achieves a remarkable 40x speed increase over the original SAM. It significantly improves mIoUs on COCO and LVIS datasets with prompt-in-the-loop knowledge distillation and a lightweight Region Proposal Network. Check out the paper and project for more details.
Introducing EdgeSAM: Advancing Machine Learning for Efficient Image Segmentation on Edge Devices
The Segment Anything Model (SAM) has been optimized for edge deployment with the development of EdgeSAM, an AI-powered model that achieves real-time image segmentation on resource-constrained edge devices such as smartphones. EdgeSAM outperforms the original SAM and Mobile-SAM, delivering a remarkable 40-fold speed increase and significantly improved mIoUs on COCO and LVIS datasets. It integrates a dynamic prompt-in-the-loop strategy and a lightweight module to address dataset bias, showcasing its efficacy for real-world applications.
Key Highlights from the Research:
- EdgeSAM is an optimized variant of SAM.
- Designed for real-time deployment on edge devices like smartphones.
- 40 times faster than the original SAM.
- Outperforms Mobile-SAM by 14 times on edge devices.
- Significantly improves mIoUs on COCO and LVIS datasets.
- Integrates dynamic prompt-in-the-loop strategy and a lightweight module.
- Explores various training configurations and prompt types.
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For more information about EdgeSAM and to access the code, models, and demo, visit the project page: EdgeSAM Project.
