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|
%global _empty_manifest_terminate_build 0
Name: python-datumaro
Version: 1.2.1
Release: 1
Summary: Dataset Management Framework (Datumaro)
License: MIT License
URL: https://github.com/openvinotoolkit/datumaro
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/31/63/1ef1d9e53b19a459827c8ef5208d91afc88cfdd722329b331a5c76c51fd2/datumaro-1.2.1.tar.gz
BuildArch: noarch
%description
# Dataset Management Framework (Datumaro)
[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml)
[](https://codecov.io/gh/openvinotoolkit/datumaro)
A framework and CLI tool to build, transform, and analyze datasets.
<!--lint disable fenced-code-flag-->
```
VOC dataset ---> Annotation tool
+ /
COCO dataset -----> Datumaro ---> dataset ------> Model training
+ \
CVAT annotations ---> Publication, statistics etc.
```
<!--lint enable fenced-code-flag-->
- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started)
- [Features](#features)
- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
- [Developer manual](https://openvinotoolkit.github.io/datumaro/api)
- [Contributing](#contributing)
## Features
[(Back to top)](#dataset-management-framework-datumaro)
- Dataset reading, writing, conversion in any direction.
- [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`)
- [Cityscapes](https://www.cityscapes-dataset.com/)
- [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`,
`captions`, `labels`, `panoptic`, `stuff`)
- [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format)
- [ImageNet](http://image-net.org/)
- [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`,
`3D raw` / `velodyne points`)
- [LabelMe](http://labelme.csail.mit.edu/Release3.0)
- [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`,
`landmarks`)
- [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`)
- [Open Images](https://storage.googleapis.com/openimages/web/download.html)
- [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html)
(`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`)
- [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md)
(`bboxes`, `masks`)
- [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)
Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats).
- Dataset building
- Merging multiple datasets into one
- Dataset filtering by a custom criteria:
- remove polygons of a certain class
- remove images without annotations of a specific class
- remove `occluded` annotations from images
- keep only vertically-oriented images
- remove small area bounding boxes from annotations
- Annotation conversions, for instance:
- polygons to instance masks and vice-versa
- apply a custom colormap for mask annotations
- rename or remove dataset labels
- Splitting a dataset into multiple subsets like `train`, `val`, and `test`:
- random split
- task-specific splits based on annotations,
which keep initial label and attribute distributions
- for classification task, based on labels
- for detection task, based on bboxes
- for re-identification task, based on labels,
avoiding having same IDs in training and test splits
- Sampling a dataset
- analyzes inference result from the given dataset
and selects the ‘best’ and the ‘least amount of’ samples for annotation.
- Select the sample that best suits model training.
- sampling with Entropy based algorithm
- Dataset quality checking
- Simple checking for errors
- Comparison with model inference
- Merging and comparison of multiple datasets
- Annotation validation based on the task type(classification, etc)
- Dataset comparison
- Dataset statistics (image mean and std, annotation statistics)
- Model integration
- Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.)
- Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421))
- RISE for classification
- RISE for object detection
> Check
[the design document](https://openvinotoolkit.github.io/datumaro/docs/design)
for a full list of features.
> Check
[the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
for usage instructions.
## Contributing
[(Back to top)](#dataset-management-framework-datumaro)
Feel free to
[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you
think something needs to be changed. You are welcome to participate in
development, instructions are available in our
[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing).
## Telemetry data collection note
The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/)
is used to collect basic information about Datumaro usage.
To enable/disable telemetry data collection please see the
[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/).
%package -n python3-datumaro
Summary: Dataset Management Framework (Datumaro)
Provides: python-datumaro
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-datumaro
# Dataset Management Framework (Datumaro)
[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml)
[](https://codecov.io/gh/openvinotoolkit/datumaro)
A framework and CLI tool to build, transform, and analyze datasets.
<!--lint disable fenced-code-flag-->
```
VOC dataset ---> Annotation tool
+ /
COCO dataset -----> Datumaro ---> dataset ------> Model training
+ \
CVAT annotations ---> Publication, statistics etc.
```
<!--lint enable fenced-code-flag-->
- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started)
- [Features](#features)
- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
- [Developer manual](https://openvinotoolkit.github.io/datumaro/api)
- [Contributing](#contributing)
## Features
[(Back to top)](#dataset-management-framework-datumaro)
- Dataset reading, writing, conversion in any direction.
- [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`)
- [Cityscapes](https://www.cityscapes-dataset.com/)
- [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`,
`captions`, `labels`, `panoptic`, `stuff`)
- [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format)
- [ImageNet](http://image-net.org/)
- [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`,
`3D raw` / `velodyne points`)
- [LabelMe](http://labelme.csail.mit.edu/Release3.0)
- [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`,
`landmarks`)
- [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`)
- [Open Images](https://storage.googleapis.com/openimages/web/download.html)
- [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html)
(`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`)
- [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md)
(`bboxes`, `masks`)
- [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)
Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats).
- Dataset building
- Merging multiple datasets into one
- Dataset filtering by a custom criteria:
- remove polygons of a certain class
- remove images without annotations of a specific class
- remove `occluded` annotations from images
- keep only vertically-oriented images
- remove small area bounding boxes from annotations
- Annotation conversions, for instance:
- polygons to instance masks and vice-versa
- apply a custom colormap for mask annotations
- rename or remove dataset labels
- Splitting a dataset into multiple subsets like `train`, `val`, and `test`:
- random split
- task-specific splits based on annotations,
which keep initial label and attribute distributions
- for classification task, based on labels
- for detection task, based on bboxes
- for re-identification task, based on labels,
avoiding having same IDs in training and test splits
- Sampling a dataset
- analyzes inference result from the given dataset
and selects the ‘best’ and the ‘least amount of’ samples for annotation.
- Select the sample that best suits model training.
- sampling with Entropy based algorithm
- Dataset quality checking
- Simple checking for errors
- Comparison with model inference
- Merging and comparison of multiple datasets
- Annotation validation based on the task type(classification, etc)
- Dataset comparison
- Dataset statistics (image mean and std, annotation statistics)
- Model integration
- Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.)
- Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421))
- RISE for classification
- RISE for object detection
> Check
[the design document](https://openvinotoolkit.github.io/datumaro/docs/design)
for a full list of features.
> Check
[the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
for usage instructions.
## Contributing
[(Back to top)](#dataset-management-framework-datumaro)
Feel free to
[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you
think something needs to be changed. You are welcome to participate in
development, instructions are available in our
[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing).
## Telemetry data collection note
The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/)
is used to collect basic information about Datumaro usage.
To enable/disable telemetry data collection please see the
[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/).
%package help
Summary: Development documents and examples for datumaro
Provides: python3-datumaro-doc
%description help
# Dataset Management Framework (Datumaro)
[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml)
[](https://codecov.io/gh/openvinotoolkit/datumaro)
A framework and CLI tool to build, transform, and analyze datasets.
<!--lint disable fenced-code-flag-->
```
VOC dataset ---> Annotation tool
+ /
COCO dataset -----> Datumaro ---> dataset ------> Model training
+ \
CVAT annotations ---> Publication, statistics etc.
```
<!--lint enable fenced-code-flag-->
- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started)
- [Features](#features)
- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
- [Developer manual](https://openvinotoolkit.github.io/datumaro/api)
- [Contributing](#contributing)
## Features
[(Back to top)](#dataset-management-framework-datumaro)
- Dataset reading, writing, conversion in any direction.
- [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`)
- [Cityscapes](https://www.cityscapes-dataset.com/)
- [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`,
`captions`, `labels`, `panoptic`, `stuff`)
- [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format)
- [ImageNet](http://image-net.org/)
- [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`,
`3D raw` / `velodyne points`)
- [LabelMe](http://labelme.csail.mit.edu/Release3.0)
- [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`,
`landmarks`)
- [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`)
- [Open Images](https://storage.googleapis.com/openimages/web/download.html)
- [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html)
(`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`)
- [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md)
(`bboxes`, `masks`)
- [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)
Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats).
- Dataset building
- Merging multiple datasets into one
- Dataset filtering by a custom criteria:
- remove polygons of a certain class
- remove images without annotations of a specific class
- remove `occluded` annotations from images
- keep only vertically-oriented images
- remove small area bounding boxes from annotations
- Annotation conversions, for instance:
- polygons to instance masks and vice-versa
- apply a custom colormap for mask annotations
- rename or remove dataset labels
- Splitting a dataset into multiple subsets like `train`, `val`, and `test`:
- random split
- task-specific splits based on annotations,
which keep initial label and attribute distributions
- for classification task, based on labels
- for detection task, based on bboxes
- for re-identification task, based on labels,
avoiding having same IDs in training and test splits
- Sampling a dataset
- analyzes inference result from the given dataset
and selects the ‘best’ and the ‘least amount of’ samples for annotation.
- Select the sample that best suits model training.
- sampling with Entropy based algorithm
- Dataset quality checking
- Simple checking for errors
- Comparison with model inference
- Merging and comparison of multiple datasets
- Annotation validation based on the task type(classification, etc)
- Dataset comparison
- Dataset statistics (image mean and std, annotation statistics)
- Model integration
- Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.)
- Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421))
- RISE for classification
- RISE for object detection
> Check
[the design document](https://openvinotoolkit.github.io/datumaro/docs/design)
for a full list of features.
> Check
[the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
for usage instructions.
## Contributing
[(Back to top)](#dataset-management-framework-datumaro)
Feel free to
[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you
think something needs to be changed. You are welcome to participate in
development, instructions are available in our
[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing).
## Telemetry data collection note
The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/)
is used to collect basic information about Datumaro usage.
To enable/disable telemetry data collection please see the
[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/).
%prep
%autosetup -n datumaro-1.2.1
%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-datumaro -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1
- Package Spec generated
|
