%global _empty_manifest_terminate_build 0
Name:		python-model-index
Version:	0.1.11
Release:	1
Summary:	Create a source of truth for ML model results and browse it on Papers with Code
License:	MIT
URL:		https://github.com/paperswithcode/model-index
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/25/91/3db595e51266e5a32f4a26e3b4c4212ba83b4ce649196e81565cf0dcdec2/model-index-0.1.11.tar.gz
BuildArch:	noarch

Requires:	python3-pyyaml
Requires:	python3-markdown
Requires:	python3-ordered-set
Requires:	python3-click

%description
# model-index: maintain a source of truth for ML models

<p align="center">

<a href="https://app.circleci.com/pipelines/github/paperswithcode/model-index">
  <img alt="Tests" src="https://img.shields.io/circleci/build/github/paperswithcode/model-index/main">
</a>

<a href="https://pypi.org/project/model-index/">
  <img alt="PyPI" src="https://img.shields.io/pypi/v/model-index">
</a>

<a href="https://model-index.readthedocs.io/en/latest/">
  <img alt="Docs" src="https://img.shields.io/readthedocs/model-index">
</a>

</p>

`model-index` has two goals:
- Make it easy to maintain a source-of-truth index of Machine Learning model metadata 
- Enable the community browse this model metadata on [Papers with Code](https://paperswithcode.com/)

The main design principle of `model-index` is **flexibility**. You can store your model metadata however is the
most convenient for you - as JSONs, YAMLs or as annotations inside markdown. `model-index` provides a convenient
way to collect all this metadata into a single file that's browsable, searchable and comparable.

You can use this library locally or choose to upload the metadata to [Papers with Code](https://paperswithcode.com)
to have your library featured on the website. 

## How it works

There is a root file for the model index: `model-index.yml` that links to (or contains) metadata. 

```yaml
Models:
  - Name: Inception v3
    Metadata:
      FLOPs: 5731284192
      Parameters: 23834568
      Training Data: ImageNet  
      Training Resources: 8x V100 GPUs
    Results:
      - Task: Image Classification
        Dataset: ImageNet
        Metrics:
          Top 1 Accuracy: 74.67%
          Top 5 Accuracy: 92.1%
    Paper: https://arxiv.org/abs/1512.00567v3
    Code: https://github.com/rwightman/pytorch-image-models/blob/timm/models/inception_v3.py#L442
    Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth 
    README: docs/inception-v3-readme.md
```

All fields except for `Name` are **optional**. You can add any fields you like, but the ones above have a 
standard meaning across different models and libraries. 

We recommend putting the `model-index.yml` file in the root of your repository (so that relative links such as 
`docs/inception-v3-readme.md` are easier to write), but you can also put it anywhere else in the repository (e.g.
in your `docs/` or `models/` folder). 

### Storing metadata in markdown files

Metadata can also be directly stored in a model's README file. For example in this `docs/rexnet.md` file:

```markdown
<!--
Type: model-index
Name: RexNet
Metadata: 
  Epochs: 400
  Batch Size: 512
Paper: https://arxiv.org/abs/2007.00992v1
-->

# Summary

Rank Expansion Networks (ReXNets) follow a set of new design 
principles for designing bottlenecks in image classification models.

## Usage

import timm
m = timm.create_model('rexnet_100', pretrained=True)
m.eval()
```

In this case, you just need to include this markdown file into the global `model-index.yml` file:

```yaml
Models:
  - docs/rexnet.md
```

## Get started

Check out our [official documentation](https://model-index.readthedocs.io/en/latest/) on how to get started. 

## Uploading to Papers with Code

To feature your library on Papers with Code, get in touch with `hello@paperswithcode.com` and the model index
of your library will be automatically included into Papers with Code. 













%package -n python3-model-index
Summary:	Create a source of truth for ML model results and browse it on Papers with Code
Provides:	python-model-index
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-model-index
# model-index: maintain a source of truth for ML models

<p align="center">

<a href="https://app.circleci.com/pipelines/github/paperswithcode/model-index">
  <img alt="Tests" src="https://img.shields.io/circleci/build/github/paperswithcode/model-index/main">
</a>

<a href="https://pypi.org/project/model-index/">
  <img alt="PyPI" src="https://img.shields.io/pypi/v/model-index">
</a>

<a href="https://model-index.readthedocs.io/en/latest/">
  <img alt="Docs" src="https://img.shields.io/readthedocs/model-index">
</a>

</p>

`model-index` has two goals:
- Make it easy to maintain a source-of-truth index of Machine Learning model metadata 
- Enable the community browse this model metadata on [Papers with Code](https://paperswithcode.com/)

The main design principle of `model-index` is **flexibility**. You can store your model metadata however is the
most convenient for you - as JSONs, YAMLs or as annotations inside markdown. `model-index` provides a convenient
way to collect all this metadata into a single file that's browsable, searchable and comparable.

You can use this library locally or choose to upload the metadata to [Papers with Code](https://paperswithcode.com)
to have your library featured on the website. 

## How it works

There is a root file for the model index: `model-index.yml` that links to (or contains) metadata. 

```yaml
Models:
  - Name: Inception v3
    Metadata:
      FLOPs: 5731284192
      Parameters: 23834568
      Training Data: ImageNet  
      Training Resources: 8x V100 GPUs
    Results:
      - Task: Image Classification
        Dataset: ImageNet
        Metrics:
          Top 1 Accuracy: 74.67%
          Top 5 Accuracy: 92.1%
    Paper: https://arxiv.org/abs/1512.00567v3
    Code: https://github.com/rwightman/pytorch-image-models/blob/timm/models/inception_v3.py#L442
    Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth 
    README: docs/inception-v3-readme.md
```

All fields except for `Name` are **optional**. You can add any fields you like, but the ones above have a 
standard meaning across different models and libraries. 

We recommend putting the `model-index.yml` file in the root of your repository (so that relative links such as 
`docs/inception-v3-readme.md` are easier to write), but you can also put it anywhere else in the repository (e.g.
in your `docs/` or `models/` folder). 

### Storing metadata in markdown files

Metadata can also be directly stored in a model's README file. For example in this `docs/rexnet.md` file:

```markdown
<!--
Type: model-index
Name: RexNet
Metadata: 
  Epochs: 400
  Batch Size: 512
Paper: https://arxiv.org/abs/2007.00992v1
-->

# Summary

Rank Expansion Networks (ReXNets) follow a set of new design 
principles for designing bottlenecks in image classification models.

## Usage

import timm
m = timm.create_model('rexnet_100', pretrained=True)
m.eval()
```

In this case, you just need to include this markdown file into the global `model-index.yml` file:

```yaml
Models:
  - docs/rexnet.md
```

## Get started

Check out our [official documentation](https://model-index.readthedocs.io/en/latest/) on how to get started. 

## Uploading to Papers with Code

To feature your library on Papers with Code, get in touch with `hello@paperswithcode.com` and the model index
of your library will be automatically included into Papers with Code. 













%package help
Summary:	Development documents and examples for model-index
Provides:	python3-model-index-doc
%description help
# model-index: maintain a source of truth for ML models

<p align="center">

<a href="https://app.circleci.com/pipelines/github/paperswithcode/model-index">
  <img alt="Tests" src="https://img.shields.io/circleci/build/github/paperswithcode/model-index/main">
</a>

<a href="https://pypi.org/project/model-index/">
  <img alt="PyPI" src="https://img.shields.io/pypi/v/model-index">
</a>

<a href="https://model-index.readthedocs.io/en/latest/">
  <img alt="Docs" src="https://img.shields.io/readthedocs/model-index">
</a>

</p>

`model-index` has two goals:
- Make it easy to maintain a source-of-truth index of Machine Learning model metadata 
- Enable the community browse this model metadata on [Papers with Code](https://paperswithcode.com/)

The main design principle of `model-index` is **flexibility**. You can store your model metadata however is the
most convenient for you - as JSONs, YAMLs or as annotations inside markdown. `model-index` provides a convenient
way to collect all this metadata into a single file that's browsable, searchable and comparable.

You can use this library locally or choose to upload the metadata to [Papers with Code](https://paperswithcode.com)
to have your library featured on the website. 

## How it works

There is a root file for the model index: `model-index.yml` that links to (or contains) metadata. 

```yaml
Models:
  - Name: Inception v3
    Metadata:
      FLOPs: 5731284192
      Parameters: 23834568
      Training Data: ImageNet  
      Training Resources: 8x V100 GPUs
    Results:
      - Task: Image Classification
        Dataset: ImageNet
        Metrics:
          Top 1 Accuracy: 74.67%
          Top 5 Accuracy: 92.1%
    Paper: https://arxiv.org/abs/1512.00567v3
    Code: https://github.com/rwightman/pytorch-image-models/blob/timm/models/inception_v3.py#L442
    Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth 
    README: docs/inception-v3-readme.md
```

All fields except for `Name` are **optional**. You can add any fields you like, but the ones above have a 
standard meaning across different models and libraries. 

We recommend putting the `model-index.yml` file in the root of your repository (so that relative links such as 
`docs/inception-v3-readme.md` are easier to write), but you can also put it anywhere else in the repository (e.g.
in your `docs/` or `models/` folder). 

### Storing metadata in markdown files

Metadata can also be directly stored in a model's README file. For example in this `docs/rexnet.md` file:

```markdown
<!--
Type: model-index
Name: RexNet
Metadata: 
  Epochs: 400
  Batch Size: 512
Paper: https://arxiv.org/abs/2007.00992v1
-->

# Summary

Rank Expansion Networks (ReXNets) follow a set of new design 
principles for designing bottlenecks in image classification models.

## Usage

import timm
m = timm.create_model('rexnet_100', pretrained=True)
m.eval()
```

In this case, you just need to include this markdown file into the global `model-index.yml` file:

```yaml
Models:
  - docs/rexnet.md
```

## Get started

Check out our [official documentation](https://model-index.readthedocs.io/en/latest/) on how to get started. 

## Uploading to Papers with Code

To feature your library on Papers with Code, get in touch with `hello@paperswithcode.com` and the model index
of your library will be automatically included into Papers with Code. 













%prep
%autosetup -n model-index-0.1.11

%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-model-index -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.11-1
- Package Spec generated