https://github.com/cvhub520/sam-hq

This project showcases the deployment of the encoder and decoder model using ONNXRUNTIME in Python.

https://github.com/cvhub520/sam-hq

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
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    Found codemeta.json file
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    Links to: arxiv.org
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    Low similarity (10.6%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

This project showcases the deployment of the encoder and decoder model using ONNXRUNTIME in Python.

Basic Info
  • Host: GitHub
  • Owner: CVHub520
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 47.5 MB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

Segment Anything in High Quality

PWC Open In Colab Huggingfaces Open in OpenXLab

Segment Anything in High Quality
NeurIPS 2023
ETH Zurich & HKUST

ONNX export

The HQ-SAM's heavy encoder and lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime. Export the model with run.sh

[Option-1]

You can see the example notebook for details on how to combine image preprocessing via HQ-SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.

[Option-2]

If you are targeting a deployment scenario, it's recommended refer to the example in scripts/main.py. Similarly, you can find the execution command in the run.sh script.

example4_box example4_point1 example4_point2

OnnxRuntime Demo Results

Citation

If you find HQ-SAM useful in your research or refer to the provided baseline results, please star :star: this repository and consider citing :pencil:: @article{sam_hq, title={Segment Anything in High Quality}, author={Ke, Lei and Ye, Mingqiao and Danelljan, Martin and Liu, Yifan and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher}, journal = {arXiv:2306.01567}, year = {2023} }

Owner

  • Name: CVHub
  • Login: CVHub520
  • Kind: user
  • Company: CVHub

Welcome to CVHub!

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