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yunchuyunchu
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Merge branch 'releases/1.4.0' into mergeback/1.4.4rc2
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‎.github/workflows/publish.yml‎

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name: Build and upload to internal PyPI
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name: Build and upload to PyPI
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on:
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workflow_dispatch: # run on request (no need for PR)
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name: Publish package
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needs: [build_wheels, build_sdist]
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environment: pypi
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runs-on: [self-hosted, linux, x64, dev]
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runs-on: ubuntu-latest
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permissions: write-all
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steps:
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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- name: Install dependencies
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run: python -m pip install twine
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- name: Download artifacts
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uses: actions/download-artifact@v3
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with:
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# unpacks default artifact into dist/
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# if `name: artifact` is omitted, the action will create extra parent dir
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name: artifact
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path: dist
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# to determine where to publish the source distribution to PyPI or TestPyPI
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- name: Check tag
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id: check-tag
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uses: actions-ecosystem/action-regex-match@v2
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tag: ${{ github.ref }}
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overwrite: true
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file_glob: true
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- name: Check dist contents
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run: twine check dist/*
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- name: Publish package dist to internal PyPI
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run: |
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export no_proxy=${{ secrets.PYPI_HOST }}
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export REPOSITORY_URL=http://${{ secrets.PYPI_HOST }}:${{ secrets.PYPI_PORT }}
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twine upload --verbose --repository-url $REPOSITORY_URL dist/* -u ${{ secrets.PYPI_USER }} -p ${{ secrets.PYPI_PASSWORD }}
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- name: Clean up dist
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if: ${{ always() }}
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run: |
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if OUTPUT=$(ls | grep -c dist)
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then
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echo "Cleaning up dist directory"
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rm -r dist
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fi
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- name: Publish package distributions to PyPI
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if: ${{ steps.check-tag.outputs.match != '' }}
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uses: pypa/[email protected]
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}
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- name: Publish package distributions to TestPyPI
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if: ${{ steps.check-tag.outputs.match == '' }}
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uses: pypa/[email protected]
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with:
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password: ${{ secrets.TESTPYPI_API_TOKEN }}
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repository-url: https://test.pypi.org/legacy/
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verbose: true

‎.github/workflows/publish_internal.yml‎

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name: Build and upload to internal PyPI
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on:
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workflow_dispatch: # run on request (no need for PR)
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jobs:
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build_wheels:
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name: Build wheels
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Build wheels
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uses: pypa/[email protected]
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- uses: actions/upload-artifact@v3
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with:
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path: ./wheelhouse/*.whl
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build_sdist:
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name: Build source distribution
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Set up Python 3.10
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uses: actions/setup-python@v3
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with:
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python-version: "3.10"
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- name: Install pypa/build
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run: python -m pip install build
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- name: Build sdist
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run: python -m build --sdist
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- uses: actions/upload-artifact@v3
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with:
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path: dist/*.tar.gz
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publish_package:
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name: Publish package
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needs: [build_wheels, build_sdist]
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environment: pypi
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runs-on: [self-hosted, linux, x64, dev]
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permissions: write-all
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steps:
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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- name: Install dependencies
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run: python -m pip install twine
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- name: Download artifacts
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uses: actions/download-artifact@v3
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with:
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# unpacks default artifact into dist/
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# if `name: artifact` is omitted, the action will create extra parent dir
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name: artifact
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path: dist
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- name: Check tag
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id: check-tag
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uses: actions-ecosystem/action-regex-match@v2
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with:
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text: ${{ github.ref }}
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regex: '^refs/heads/releases/[0-9]+\.[0-9]+\.[0-9]+(\.[0-9]+rc[0-9]+|rc[0-9]+)?$'
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- name: Check dist contents
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run: twine check dist/*
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- name: Publish package dist to internal PyPI
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if: ${{ steps.check-tag.outputs.match != '' }}
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run: |
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export no_proxy=${{ secrets.PYPI_HOST }}
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export REPOSITORY_URL=http://${{ secrets.PYPI_HOST }}:${{ secrets.PYPI_PORT }}
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twine upload --verbose --repository-url $REPOSITORY_URL dist/* -u ${{ secrets.PYPI_USER }} -p ${{ secrets.PYPI_PASSWORD }}
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- name: Publish package distributions to TestPyPI
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if: ${{ steps.check-tag.outputs.match == '' }}
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run: |
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export REPOSITORY_URL=https://test.pypi.org/legacy/
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twine upload --verbose --repository-url $REPOSITORY_URL dist/* -u __token__ -p ${{ secrets.TESTPYPI_API_TOKEN }}
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- name: Clean up dist
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if: ${{ always() }}
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run: |
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if OUTPUT=$(ls | grep -c dist)
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then
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echo "Cleaning up dist directory"
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rm -r dist
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fi

‎README.md‎

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---
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[Key Features](#key-features) •
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[Installation](https://openvinotoolkit.github.io/training_extensions/stable/guide/get_started/installation.html) •
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[Documentation](https://openvinotoolkit.github.io/training_extensions/stable/index.html) •
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[Installation](https://openvinotoolkit.github.io/training_extensions/1.5.0/guide/get_started/installation.html) •
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[Documentation](https://openvinotoolkit.github.io/training_extensions/1.5.0/index.html) •
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[License](#license)
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[![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
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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://openvinotoolkit.github.io/training_extensions/stable/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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- **Action recognition** including action classification and detection
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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/stable/guide/explanation/algorithms/index.html):
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OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.5.0/guide/explanation/algorithms/index.html):
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- **Supervised**, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
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- **Semi-supervised learning**
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- **Self-supervised learning**
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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 with appropriate input size to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
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- [Auto-configuration](https://openvinotoolkit.github.io/training_extensions/stable/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model with appropriate input size to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
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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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- **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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- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
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- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/stable/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
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---
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## Getting Started
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### Installation
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Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/stable/guide/get_started/installation.html).
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Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.5.0/guide/get_started/installation.html).
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Note: Python 3.8, 3.9 and 3.10 were tested, along with Ubuntu 18.04, 20.04 and 22.04.
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- `otx demo` allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real-life scenario.
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- `otx explain` runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions.
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You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/stable/guide/get_started/cli_commands.html).
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You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.5.0/guide/get_started/cli_commands.html).
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---
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‎docs/source/guide/release_notes/index.rst‎

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.. toctree::
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:maxdepth: 1
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v1.4.4 (4Q23)
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-------------
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- Update ModelAPI configuration
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- Add Anomaly modelAPI changes
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- Update Image numpy access
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v1.4.3 (4Q23)
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-------------
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‎requirements/base.txt‎

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# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
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# Base Algo Requirements. #
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# Base Algo Requirements. #
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natsort==8.1.*
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prettytable==3.9.*
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protobuf==3.20.*
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pyyaml
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datumaro==1.5.1rc3
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datumaro~=1.5.1rc4
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psutil==5.9.*
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scipy==1.10.*
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bayesian-optimization==1.4.*

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