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|
%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.nju.edu.cn/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
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
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
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
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.0-1
- Package Spec generated
|
