About Hexafarms
Hexafarm provides A-Z solutions for indoor farming industries.
MMSegmentation has been used as a base network to segment leaves.
This segmented leaves are used to track the growth of plants.
If you are interested, contact to [email protected]
What is special in Hexafarms'github ?
- AzureML friendly
You can use AzureML with MMsegmentaiton
- Plant leaves Customdataset Config
Customdataset config is updated for 0: background, 1: leaf
About MMSegmentation
Documentationof MMsegmentation: https://mmsegmentation.readthedocs.io/
English | 简体中文
MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.3+.
Major features
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Unified Benchmark
We provide a unified benchmark toolbox for various semantic segmentation methods.
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Modular Design
We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
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Support of multiple methods out of box
The toolbox directly supports popular and contemporary semantic segmentation frameworks, e.g. PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
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High efficiency
The training speed is faster than or comparable to other codebases.
License
This project is released under the Apache 2.0 license.
Installation
Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.
Get Started
Please see train.md and inference.md for the basic usage of MMSegmentation. There are also tutorials for customizing dataset, designing data pipeline, customizing modules, and customizing runtime. We also provide many training tricks for better training and usefule tools for deployment.
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
Citation
If you find this project useful in your research, please consider cite:
@misc{mmseg2020,
title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
author={MMSegmentation Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
year={2020}
}