seca

Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA)

https://github.com/delftcrowd/seca

Science Score: 44.0%

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
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (2.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA)

Basic Info
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  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 6
  • Releases: 0
Created about 4 years ago · Last pushed about 4 years ago
Metadata Files
Readme Changelog Contributing Citation

README.md

Global Interpretability via SEmantic Concept extraction and Analysis (SECA)

Purpose of software

Installation instructions

Users

Developers

Contributing guidelines

Owner

  • Name: TU Delft Crowd Computing Team (Kappa)
  • Login: delftcrowd
  • Kind: organization

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Agathe"
    given-names: "Balayn"
    orcid: "https://orcid.org/0000-0000-0000-0000"
title: "SECA"
version: 0.0.1
doi:
date-released:
url: "https://github.com/delftcrowd/SECA"
keywords:
  - "machine learning"
  - "explainability"

GitHub Events

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Dependencies

conda/environment.yml pypi
  • saliency *
  • symspellpy *
jupyter_book/requirements.txt pypi
  • jupyter-book *
  • matplotlib *
  • numpy *
setup.py pypi
  • graphviz *
  • keras *
  • matplotlib *
  • mlxtend *
  • notebook *
  • numpy *
  • pandas *
  • saliency *
  • scikit-image *
  • scikit-learn *
  • scipy *
  • symspellpy *
  • tensorflow *