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phenocv is a toolkits for rice high-throught phenotyping using computer vision.
phenocv is still in early development stage, and more features will be added in the future.
For label-studio semi-automatic annotation, please refer to playground.
For mmdetection training, please refer to mmdetection.
For yolo training, please refer to Ultralytics.
Support for mmdetection and label-studio will be added in the future.
Before install the package, make sure you have installed pytorch and install in the python environment with python>=3.8.
pip install phenocv
git clone https://github.com/r1cheu/phenocv.git
cd phenocv
pip install -e .
| Getting Start |  |  |
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This project is released under the AGPL 3.0 license.
If you find this project useful in your research, please consider cite:
@misc{2023phenocv,
title={Rice high-throught phenotyping computer vision toolkits},
author={RuLei Chen},
howpublished = {\url{https://github.com/r1cheu/phenocv}},
year={2023}
}
FAQs
Rice High Throughput Phenotyping Computer Vision Toolkit
We found that phenocv demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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