%global _empty_manifest_terminate_build 0 Name: python-chia Version: 2.5.0 Release: 1 Summary: Concept Hierarchies for Incremental and Active Learning License: BSD License URL: https://github.com/cabrust/chia Source0: https://mirrors.aliyun.com/pypi/web/packages/6b/3c/5d34b6146b2a6e437795093e1c5b16b3fa86cf661ae4fba5d9da5ad6c1f3/chia-2.5.0.tar.gz BuildArch: noarch Requires: python3-configuration Requires: python3-nltk Requires: python3-imageio Requires: python3-pillow Requires: python3-gputil Requires: python3-networkx Requires: python3-numpy Requires: python3-tensorflow-addons Requires: python3-tensorflow %description # CHIA: Concept Hierarchies for Incremental and Active Learning ![PyPI](https://img.shields.io/pypi/v/chia) ![PyPI - License](https://img.shields.io/pypi/l/chia) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia) ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia) ![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg) CHIA implements methods 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](http://www.inf-cv.uni-jena.de/). Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/) **Methods**\ CHIA implements: * **One-Hot Softmax Classifier** as a baseline. * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR) * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR). * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP) * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV). **Datasets**\ CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list. ## Installation and Getting Started CHIA is available on PyPI. To install, simply run: ```bash pip install chia ``` or clone this repository, and run: ```bash pip install -e . ``` To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command: ```bash python examples/experiment.py examples/configuration.json ``` After a few minutes, the last lines of output should look like this: ```text [SHUTDOWN] [Experiment] Successful: True ``` ## Documentation The following articles explain more about CHIA: * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes. * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured. * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data. ## Citation If you use CHIA for your research, kindly cite: > Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham. You can refer to the following BibTeX: ```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}, doi = {10.1007/978-3-030-41404-7_1} } ``` %package -n python3-chia Summary: Concept Hierarchies for Incremental and Active Learning Provides: python-chia BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-chia # CHIA: Concept Hierarchies for Incremental and Active Learning ![PyPI](https://img.shields.io/pypi/v/chia) ![PyPI - License](https://img.shields.io/pypi/l/chia) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia) ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia) ![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg) CHIA implements methods 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](http://www.inf-cv.uni-jena.de/). Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/) **Methods**\ CHIA implements: * **One-Hot Softmax Classifier** as a baseline. * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR) * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR). * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP) * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV). **Datasets**\ CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list. ## Installation and Getting Started CHIA is available on PyPI. To install, simply run: ```bash pip install chia ``` or clone this repository, and run: ```bash pip install -e . ``` To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command: ```bash python examples/experiment.py examples/configuration.json ``` After a few minutes, the last lines of output should look like this: ```text [SHUTDOWN] [Experiment] Successful: True ``` ## Documentation The following articles explain more about CHIA: * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes. * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured. * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data. ## Citation If you use CHIA for your research, kindly cite: > Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham. You can refer to the following BibTeX: ```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}, doi = {10.1007/978-3-030-41404-7_1} } ``` %package help Summary: Development documents and examples for chia Provides: python3-chia-doc %description help # CHIA: Concept Hierarchies for Incremental and Active Learning ![PyPI](https://img.shields.io/pypi/v/chia) ![PyPI - License](https://img.shields.io/pypi/l/chia) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia) ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia) ![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg) CHIA implements methods 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](http://www.inf-cv.uni-jena.de/). Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/) **Methods**\ CHIA implements: * **One-Hot Softmax Classifier** as a baseline. * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR) * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR). * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP) * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV). **Datasets**\ CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list. ## Installation and Getting Started CHIA is available on PyPI. To install, simply run: ```bash pip install chia ``` or clone this repository, and run: ```bash pip install -e . ``` To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command: ```bash python examples/experiment.py examples/configuration.json ``` After a few minutes, the last lines of output should look like this: ```text [SHUTDOWN] [Experiment] Successful: True ``` ## Documentation The following articles explain more about CHIA: * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes. * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured. * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data. ## Citation If you use CHIA for your research, kindly cite: > Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham. You can refer to the following BibTeX: ```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}, doi = {10.1007/978-3-030-41404-7_1} } ``` %prep %autosetup -n chia-2.5.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-chia -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Jun 09 2023 Python_Bot - 2.5.0-1 - Package Spec generated