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Update documentation and references to Open-Edge Platform (#4331)
* Update documentation and references to Open-Edge Platform- Updated dataset format links in auto_configuration.rst, models_optimization.rst, and various algorithm documentation to point to the Open-Edge Platform repository.
- Changed references from openvinotoolkit to open-edge-platform in multiple files including anomaly detection, classification, object detection, segmentation, and product design documentation.
- Modified installation instructions and CLI command references to reflect the new repository URL.
- Adjusted the help formatter to display the updated GitHub repository link.
- Ensured all links to Datumaro and related resources are consistent with the new repository structure.
Copy file name to clipboardExpand all lines: .github/pull_request_template.md
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@@ -23,13 +23,13 @@ not fully covered by unit tests or manual testing can be complicated. -->
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-[ ] I have added unit tests to cover my changes.
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-[ ] I have added integration tests to cover my changes.
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-[ ] I have ran e2e tests and there is no issues.
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-[ ] I have added the description of my changes into CHANGELOG in my target branch (e.g., [CHANGELOG](https://github.com/openvinotoolkit/training_extensions/blob/develop/CHANGELOG.md) in develop).
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-[ ] I have updated the documentation in my target branch accordingly (e.g., [documentation](https://github.com/openvinotoolkit/training_extensions/tree/develop/docs) in develop).
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-[ ] I have added the description of my changes into CHANGELOG in my target branch (e.g., [CHANGELOG](https://github.com/open-edge-platform/training_extensions/blob/develop/CHANGELOG.md) in develop).
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-[ ] I have updated the documentation in my target branch accordingly (e.g., [documentation](https://github.com/open-edge-platform/training_extensions/tree/develop/docs) in develop).
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-[ ] I have [linked related issues](https://help.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword).
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### License
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-[ ] I submit _my code changes_ under the same [Apache License](https://github.com/openvinotoolkit/training_extensions/blob/develop/LICENSE) that covers the project.
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-[ ] I submit _my code changes_ under the same [Apache License](https://github.com/open-edge-platform/training_extensions/blob/develop/LICENSE) that covers the project.
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Feel free to contact the maintainers if that's a concern.
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-[ ] I have updated the license header for each file (see an example below).
@@ -42,7 +42,7 @@ If you are an experienced user, you can configure your own model based on [torch
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Furthermore, OpenVINO™ Training Extensions provides automatic configuration for ease of use.
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The framework will analyze your dataset and identify the most suitable model and figure out the best input size setting and other hyper-parameters.
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The development team is continuously extending this [Auto-configuration](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html) functionalities to make training as simple as possible so that single CLI command can obtain accurate, efficient and robust models ready to be integrated into your project.
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The development team is continuously extending this [Auto-configuration](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html) functionalities to make training as simple as possible so that single CLI command can obtain accurate, efficient and robust models ready to be integrated into your project.
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### Key Features
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@@ -54,22 +54,22 @@ OpenVINO™ Training Extensions supports the following computer vision tasks:
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-**Instance segmentation** including tiling algorithm support
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-**Anomaly recognition** tasks including anomaly classification, detection and segmentation
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OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/algorithms/index.html):
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OpenVINO™ Training Extensions supports the [following learning methods](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/algorithms/index.html):
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-**Supervised**, incremental training, which includes class incremental scenario.
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OpenVINO™ Training Extensions provides the following usability features:
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-[Auto-configuration](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model to provide the best accuracy/speed trade-off.
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-[Datumaro](https://openvinotoolkit.github.io/datumaro/stable/index.html) data frontend: OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We are constantly working to extend supported formats to give more freedom of datasets format choice.
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-[Auto-configuration](https://open-edge-platform.github.io/training_extensions/latest/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model to provide the best accuracy/speed trade-off.
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-[Datumaro](https://open-edge-platform.github.io/datumaro/stable/index.html) data frontend: OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We are constantly working to extend supported formats to give more freedom of datasets format choice.
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-**Distributed training** to accelerate the training process when you have multiple GPUs
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-**Mixed-precision training** to save GPUs memory and use larger batch sizes
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---
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## Installation
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Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/installation.html).
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Please refer to the [installation guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/installation.html).
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If you want to make changes to the library, then a local installation is recommended.
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<details>
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# ...
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# Clone the repository and install in editable mode
otx train --help -vv # Print all configurable parameters
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```
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You can find details with examples in the [CLI Guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/cli_commands.html). and [API Quick-Guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/api_tutorial.html).
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You can find details with examples in the [CLI Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/cli_commands.html). and [API Quick-Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/api_tutorial.html).
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Below is how to train with auto-configuration, which is provided to users with datasets and tasks:
For more examples, see documentation: [API Quick-Guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/api_tutorial.html)
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For more examples, see documentation: [API Quick-Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/api_tutorial.html)
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</details>
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@@ -155,11 +155,11 @@ For more examples, see documentation: [API Quick-Guide](https://openvinotoolkit.
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otx train --data_root data/wgisd --task DETECTION
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```
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For more examples, see documentation: [CLI Guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/cli_commands.html)
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For more examples, see documentation: [CLI Guide](https://open-edge-platform.github.io/training_extensions/latest/guide/get_started/cli_commands.html)
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</details>
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In addition to the examples above, please refer to the documentation for tutorials on using custom models, training parameter overrides, and [tutorial per task types](https://openvinotoolkit.github.io/training_extensions/latest/guide/tutorials/base/how_to_train/index.html), etc.
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In addition to the examples above, please refer to the documentation for tutorials on using custom models, training parameter overrides, and [tutorial per task types](https://open-edge-platform.github.io/training_extensions/latest/guide/tutorials/base/how_to_train/index.html), etc.
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---
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@@ -178,7 +178,7 @@ By contributing to the project, you agree to the license and copyright terms the
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## Issues / Discussions
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Please use [Issues](https://github.com/openvinotoolkit/training_extensions/issues/new/choose) tab for your bug reporting, feature requesting, or any questions.
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Please use [Issues](https://github.com/open-edge-platform/training_extensions/issues/new/choose) tab for your bug reporting, feature requesting, or any questions.
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---
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@@ -196,8 +196,8 @@ For those who would like to contribute to the library, see [CONTRIBUTING.md](CON
If we have a dataset format occluded with other tasks, for example ``COCO`` format, we should directly emphasize the task type. If not, OpenVINO™ Training Extensions automatically chooses the task type that you might not intend:
Copy file name to clipboardExpand all lines: docs/source/guide/explanation/algorithms/classification/hierarhical_classification.rst
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@@ -38,8 +38,8 @@ Dataset Format
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**************
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.. _hierarchical_dataset:
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For hierarchical image classification, we created our custom dataset format that is supported by `Datumaro <https://github.com/openvinotoolkit/datumaro>`_.
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An example of the annotations format and dataset structure can be found in our `sample <https://github.com/openvinotoolkit/training_extensions/tree/develop/tests/assets/hlabel_classification>`_.
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For hierarchical image classification, we created our custom dataset format that is supported by `Datumaro <https://github.com/open-edge-platform/datumaro>`_.
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An example of the annotations format and dataset structure can be found in our `sample <https://github.com/open-edge-platform/training_extensions/tree/develop/tests/assets/hlabel_classification>`_.
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