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segmetrics

Image segmentation and object detection performance measures

https://github.com/bmcv/segmetrics

Science Score: 75.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization bmcv has institutional domain (www.bioquant.uni-heidelberg.de)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (5.4%) to scientific vocabulary

Keywords

image-segmentation instance-segmentation object-segmentation segmentation-accuracy segmentation-analysis segmentation-evaluation
Last synced: 6 months ago · JSON representation ·

Repository

Image segmentation and object detection performance measures

Basic Info
  • Host: GitHub
  • Owner: BMCV
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 2.02 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 0
  • Open Issues: 2
  • Releases: 3
Topics
image-segmentation instance-segmentation object-segmentation segmentation-accuracy segmentation-analysis segmentation-evaluation
Created about 5 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation Codeowners

README.rst 

.. raw:: html

  

    
Image segmentation and object detection performance measures

    

      segmetrics

      
      
      DOI

      
      
      
      
    

  


The goal of this package is to provide easy-to-use tools for evaluation of the performance of segmentation methods in biomedical image analysis and beyond, and to fasciliate the comparison of different methods by providing standardized implementations. This package currently only supports 2-D image data.

This tool is also available as a `web-app for the Galaxy platform`_.

.. _web-app for the Galaxy platform: https://usegalaxy.eu/root?tool_id=toolshed.g2.bx.psu.edu/repos/imgteam/segmetrics/ip_segmetrics

The documentation is available here: https://segmetrics.readthedocs.io

Use ``python -m unittest`` to run the test suite.

Contributions:
""""""""""""""

Contributions should be made against the ``develop`` branch, so that the documentation build on readthedocs.io is triggered, the documentation is built and reviewed (see `here `_), before ``develop`` is merged into ``master``. This ensures that the ``master`` branch always has an up-to-date documentation.

----

.. raw:: html

  

    Copyright (c) 2017-2025 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University

    This work is licensed under the terms of the MIT license. For a copy, see LICENSE.

Owner

  • Name: BMCV
  • Login: BMCV
  • Kind: organization
  • Location: Heidelberg, Germany

Biomedical Computer Vision Group

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "segmetrics"
doi: "10.5281/zenodo.10817137"
version: 1.5
date-released: 2024-03-14
url: "https://github.com/BMCV/segmetrics"
authors:
- family-names: "Kostrykin"
  given-names: "Leonid"
  orcid: "https://orcid.org/0000-0003-1323-3762"
- family-names: "Wollmann"
  given-names: "Thomas"
  orcid: "https://orcid.org/0000-0002-4741-3844"

GitHub Events

Total
  • Delete event: 2
  • Issue comment event: 7
  • Push event: 12
  • Pull request event: 4
  • Create event: 2
Last Year
  • Delete event: 2
  • Issue comment event: 7
  • Push event: 12
  • Pull request event: 4
  • Create event: 2

Dependencies

requirements.txt pypi
  • dill *
  • numpy >=1.18
  • scikit-image >=0.18
  • scikit-learn *
  • scipy *
setup.py pypi