Merge branch 'develop' into pf/adding_anomaly_training_tests

.github/workflows/stale_marker.yml

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+name: "Close stale issues and PR"
+on:
+  schedule:
+    - cron: "30 1 * * *"
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v4
+        with:
+          stale-issue-message: "This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 7 days."
+          stale-pr-message: "This PR is stale because it has been open 90 days with no activity."
+          close-issue-message: "This issue was closed because it has been stalled for 30 days with no activity."
+          days-before-issue-stale: 90
+          days-before-pr-stale: 90
+          days-before-issue-close: 30
+          days-before-pr-close: -1
+          start-date: "2022-02-15T00:00:00Z"

OTE_landing_page.md

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+# OpenVINO™ Training Extensions {#ote_documentation}
+[![python](https://img.shields.io/badge/python-3.8%2B-green)]()
+[![black](https://img.shields.io/badge/code%20style-black-000000.svg)]()
+[![mypy](https://img.shields.io/badge/%20type_checker-mypy-%231674b1?style=flat)]()
+[![openvino](https://img.shields.io/badge/openvino-2021.4-purple)]()
+
+OpenVINO™ Training Extensions (OTE) provide a suite of advanced algorithms to train
+Deep Learning models and convert them using the [OpenVINO™
+toolkit](https://software.intel.com/en-us/openvino-toolkit) for optimized
+inference. It allows you to export and convert the models to the needed format. OTE independently create and train the model. It is open-sourced and available on [GitHub](https://github.com/openvinotoolkit/training_extensions).
+
+## Detailed Workflow
+![](training_extensions_framework.png)
+
+1. To start working with OTE, prepare and annotate your dataset. For example, on CVAT.
+
+2. OTE train the model, using training interface, and evaluate the model quality on your dataset, using evaluation and inference interfaces.
+
+Note: prepare a separate dataset or split the dataset you have for more accurate quality evaluation.
+
+3. Having successful evaluation results received, you have an opportunity to deploy your model or continue optimizing it, using NNCF and POT. For more information about these frameworks, go to [Optimization Guide](https://docs.openvino.ai/nightly/openvino_docs_model_optimization_guide.html).
+
+If the results are unsatisfactory, add datasets and perform the same steps, starting with dataset annotation.
+
+## OTE Components
+* [OTE SDK](https://github.com/openvinotoolkit/training_extensions/tree/master/ote_sdk)
+* [OTE CLI](https://github.com/openvinotoolkit/training_extensions/tree/master/ote_cli)
+* [OTE Algorithms](https://github.com/openvinotoolkit/training_extensions/tree/master/external)
+
+## Get Started
+## Prerequisites
+* Ubuntu 18.04 / 20.04
+* Python 3.8+
+* [CUDA Toolkit 11.1](https://developer.nvidia.com/cuda-11.1.1-download-archive) - for training on GPU
+In order to get started with OpenVINO™ Training Extensions click [here](QUICK_START_GUIDE.md).
+
+## Installation
+
+1. Clone repository in the working directory by running the following:
+    ```
+    git clone https://github.com/openvinotoolkit/training_extensions.git
+    cd training_extensions
+    git checkout -b develop origin/develop
+    git submodule update --init --recursive
+    ```
+
+2. Install prerequisites by running the following:
+    ```
+    sudo apt-get install python3-pip python3-venv
+    ```
+
+3. Search for available scripts that create python virtual environments for different task types:
+   ```bash
+   find external/ -name init_venv.sh
+   ```
+
+   Sample output:
+   ```
+   external/mmdetection/init_venv.sh
+   external/mmsegmentation/init_venv.sh
+   external/deep-object-reid/init_venv.sh
+   ```
+
+4. Create, activate Object Detection virtual environment, and install `ote_cli`:
+   ```
+   ./external/mmdetection/init_venv.sh det_venv
+   source det_venv/bin/activate
+   pip3 install -e ote_cli/
+   ```
+To learn more about OTE CLI commands go to [GitHub](https://github.com/openvinotoolkit/training_extensions/blob/master/QUICK_START_GUIDE.md).
+
+## Tutorials
+[Object Detection](https://github.com/openvinotoolkit/training_extensions/blob/master/ote_cli/notebooks/train.ipynb)
+
+## Contribution
+If you want to contribute, refer to [Contributing guide](https://github.com/openvinotoolkit/training_extensions/blob/master/CONTRIBUTING.md) before starting work on a pull request.
+
+Deep Learning Deployment Toolkit is licensed under [Apache License Version 2.0](https://github.com/openvinotoolkit/training_extensions/blob/master/LICENSE).
+By contributing to the project, you agree to the license and copyright terms therein
+and release your contribution under these terms.