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 codecov

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 == 0.7.1
  • 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 creates a virtual environment and install CHIA for you.

Getting Started

To run the example experiment which makes sure that everything works, use the following command:

python examples/experiment.py examples/configuration.json

After a few minutes, the last lines of output should look like this:

[DEBUG] [ExceptionShroud]: Leaving exception shroud without exception
[SHUTDOWN] [Experiment] Successful: True

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-2.0rc17.tar.gz (66.6 kB view details)

Uploaded Source

Built Distribution

chia-2.0rc17-py3-none-any.whl (88.3 kB view details)

Uploaded Python 3

File details

Details for the file chia-2.0rc17.tar.gz.

File metadata

  • Download URL: chia-2.0rc17.tar.gz
  • Upload date:
  • Size: 66.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.0 requests/2.24.0 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.9.0

File hashes

Hashes for chia-2.0rc17.tar.gz
Algorithm Hash digest
SHA256 5a781ff73b087beeffe0a3fa3de4b12116aafd133c2b718427eac007fa2ec9a6
MD5 8d26da9a86dff74fa691237a1a221f57
BLAKE2b-256 d853ee4df3ac7c422e6611110d07e29d584dcb8cb2b054804f8b4f0a3b01867e

See more details on using hashes here.

File details

Details for the file chia-2.0rc17-py3-none-any.whl.

File metadata

  • Download URL: chia-2.0rc17-py3-none-any.whl
  • Upload date:
  • Size: 88.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.0 requests/2.24.0 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.9.0

File hashes

Hashes for chia-2.0rc17-py3-none-any.whl
Algorithm Hash digest
SHA256 a28e298adb19e4ff2e8e1de7552fa45d3efb9091179cfce8e7ddba7a0f9561dc
MD5 039c9e108086a11aa9c46172366707c1
BLAKE2b-256 2ef8555198f16fda7d65bb3dc8842105d9fe0793f24447078aa8ecb8b2ec4818

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