Skip to main content

Concept Hierarchies for Incremental and Active Learning

Project description

CHIA: Concept Hierarchies for Incremental and Active Learning

PyPI PyPI - License PyPI - Python Version Code Climate maintainability

CHIA is a collection of methods and helper functions centered around hierarchical classification in a lifelong learning environment. It forms the basis for some of the experiments and tools developed at Computer Vision Group Jena.

Requirements

CHIA depends on:

  • python-configuration ~= 0.7
  • nltk ~= 3.5
  • imageio ~= 2.6
  • pillow ~= 7.1.0
  • gputil ~= 1.4.0
  • networkx ~= 2.4
  • numpy ~= 1.18.5
  • tensorflow-addons == 0.11.1
  • tensorflow == 2.3.0

Optional dependencies:

  • tables ~= 3.6.1
  • pandas ~= 1.0.4
  • sacred ~= 0.8.1
  • pyqt5 ~= 5.15.0
  • scikit-image ~= 0.17.2
  • scikit-learn ~= 0.23.1
  • scipy == 1.4.1
  • matplotlib ~= 3.2.1

Installation

To install, simply run:

pip install chia

or clone this repository, and run:

pip install -U pip setuptools
python setup.py develop

We also include the shell script quick-venv.sh, which will create a virtual environment and install CHIA for you.

Citation

If you use CHIA for your research, kindly cite:

Brust, C. A., & Denzler, J. (2019, November). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition (pp. 3-16). Springer, Cham.

You can refer to the following BibTeX:

@inproceedings{Brust2019IDK,
author = {Clemens-Alexander Brust and Joachim Denzler},
booktitle = {Asian Conference on Pattern Recognition (ACPR)},
title = {Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers},
year = {2019},
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chia-2.0rc4.tar.gz (61.0 kB view details)

Uploaded Source

Built Distribution

chia-2.0rc4-py3-none-any.whl (80.9 kB view details)

Uploaded Python 3

File details

Details for the file chia-2.0rc4.tar.gz.

File metadata

  • Download URL: chia-2.0rc4.tar.gz
  • Upload date:
  • Size: 61.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for chia-2.0rc4.tar.gz
Algorithm Hash digest
SHA256 95ff8549f264ae5c4d59fe75cde3e7e91234adaadbad3d9c5c47afc2b56564e3
MD5 838e5007c64a78b03d0a54d4890c3f9d
BLAKE2b-256 0c6c74ee0a95869ec64f3371fcca7167e747f0d66cf7d4929232980b86283076

See more details on using hashes here.

File details

Details for the file chia-2.0rc4-py3-none-any.whl.

File metadata

  • Download URL: chia-2.0rc4-py3-none-any.whl
  • Upload date:
  • Size: 80.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for chia-2.0rc4-py3-none-any.whl
Algorithm Hash digest
SHA256 9b57ffcc6f2da4e77c4b9077f4b5875b07abe54322db5a06ceda6ab8c96e1c6f
MD5 b372a6559edb74e3f58eb6addf666bfa
BLAKE2b-256 5df4544c39c48b41a4037032eb32918cc50573b6515373067046ab66b5473619

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page