Summary of Efficientvit-sam: Accelerated Segment Anything Model Without Accuracy Loss, by Zhuoyang Zhang et al.
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss
by Zhuoyang Zhang, Han Cai, Song Han
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary EfficientViT-SAM is a novel family of accelerated segment anything models that leverages the efficiency and capacity of EfficientViT to accelerate image processing. By retaining SAM’s lightweight prompt encoder and mask decoder, while replacing the heavy image encoder with EfficientViT, the model achieves significant speedup without compromising performance. In end-to-end training on the SA-1B dataset, EfficientViT-SAM delivers a 48.9x measured TensorRT speedup on A100 GPU compared to SAM-ViT-H. The code and pre-trained models are released at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to process images quickly without losing accuracy. It combines two existing methods, EfficientViT and SAM, to create a faster model called EfficientViT-SAM. This new model is trained on a dataset of images and does not sacrifice performance for speed. In fact, it’s much faster than the original model, with a 48.9x speedup on certain computers. You can find the code and pre-trained models at this link. |
Keywords
* Artificial intelligence * Decoder * Encoder * Mask * Prompt * Sam * Vit
