%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) [![Build status](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml/badge.svg)](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) [![codecov](https://codecov.io/gh/openvinotoolkit/datumaro/branch/develop/graph/badge.svg?token=FG25VU096Q)](https://codecov.io/gh/openvinotoolkit/datumaro) A framework and CLI tool to build, transform, and analyze datasets. ``` VOC dataset ---> Annotation tool + / COCO dataset -----> Datumaro ---> dataset ------> Model training + \ CVAT annotations ---> Publication, statistics etc. ``` - [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) [![Build status](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml/badge.svg)](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) [![codecov](https://codecov.io/gh/openvinotoolkit/datumaro/branch/develop/graph/badge.svg?token=FG25VU096Q)](https://codecov.io/gh/openvinotoolkit/datumaro) A framework and CLI tool to build, transform, and analyze datasets. ``` VOC dataset ---> Annotation tool + / COCO dataset -----> Datumaro ---> dataset ------> Model training + \ CVAT annotations ---> Publication, statistics etc. ``` - [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) [![Build status](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml/badge.svg)](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) [![codecov](https://codecov.io/gh/openvinotoolkit/datumaro/branch/develop/graph/badge.svg?token=FG25VU096Q)](https://codecov.io/gh/openvinotoolkit/datumaro) A framework and CLI tool to build, transform, and analyze datasets. ``` VOC dataset ---> Annotation tool + / COCO dataset -----> Datumaro ---> dataset ------> Model training + \ CVAT annotations ---> Publication, statistics etc. ``` - [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 - 1.2.1-1 - Package Spec generated