metadata

title: Repvit Sam
emoji: 🚀
colorFrom: yellow
colorTo: yellow
sdk: gradio
sdk_version: 4.8.0
app_file: app.py
pinned: false
license: mit

RepViT-SAM: Towards Real-Time Segmenting Anything

Official PyTorch implementation of RepViT-SAM, from the following paper:

RepViT-SAM: Towards Real-Time Segmenting Anything.
Ao Wang, Hui Chen, Zijia Lin, Hengjun Pu, and Guiguang Ding
[arXiv]

Abstract

Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting for practical applications. MobileSAM proposes to replace the heavyweight image encoder in SAM with TinyViT by employing distillation, which results in a significant reduction in computational requirements. However, its deployment on resource-constrained mobile devices still encounters challenges due to the substantial memory and computational overhead caused by self-attention mechanisms. Recently, RepViT achieves the state-of-the-art performance and latency trade-off on mobile devices by incorporating efficient architectural designs of ViTs into CNNs. Here, to achieve real-time segmenting anything on mobile devices, following, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM can enjoy significantly better zero-shot transfer capability than MobileSAM, along with nearly $10\times$ faster inference speed.

Installation

git clone https://github.com/THU-MIG/RepViT
cd sam && pip install -e .
# download pretrained checkpoint
mkdir weights && cd weights
wget https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_sam.pt

Demo

Our Hugging Face demo is here

python app/app.py

CoreML export

Please refer to coreml_example.ipynb

Latency comparisons

Comparison between RepViT-SAM and others in terms of latency. The latency (ms) is measured with the standard resolution of 1024 x 1024 on iPhone 12 and Macbook M1 Pro by Core ML Tools. OOM means out of memory.

Platform	Image encoder			Mask decoder
iPhone	48.9ms	OOM	OOM	11.6ms
Macbook	44.8ms	482.2ms	6249.5ms	11.8ms

Acknowledgement

The code base is partly built with SAM and MobileSAM.

Thanks for the great implementations!

Citation

If our code or models help your work, please cite our paper:

@misc{wang2023repvit,
      title={RepViT: Revisiting Mobile CNN From ViT Perspective}, 
      author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
      year={2023},
      eprint={2307.09283},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}