This open-source version includes several components: namely [Model Optimizer], [OpenVINO™ Runtime], [Post-Training Optimization Tool], as well as CPU, GPU, GNA, multi device and heterogeneous plugins to accelerate deep learning inference on Intel® CPUs and Intel® Processor Graphics.
* [OpenVINO™ Runtime] - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
* [core](./src/core) - provides the base API for model representation and modification.
* [inference](./src/inference) - provides an API to infer models on the device.
* [transformations](./src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
* [low precision transformations](./src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
* [bindings](./src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
* [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
* [Model Optimizer] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
* [Post-Training Optimization Tool] - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization.
<td>Auto batch plugin performs on-the-fly automatic batching (i.e. grouping inference requests together) to improve device utilization, with no programming effort from the user</td>
The latest documentation for OpenVINO™ Toolkit is available [here](https://docs.openvino.ai/). This documentation contains detailed information about all OpenVINO components and provides all the important information you may need to create an application based on binary OpenVINO distribution or own OpenVINO version without source code modification.
[Developer documentation](./docs/dev/index.md) contains information about architectural decisions which are applied inside the OpenVINO components. This documentation has all necessary information which could be needed in order to contribute to OpenVINO.
* [Intel® Distribution of OpenVINO™ toolkit Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
* [Intel® Distribution of OpenVINO™ toolkit Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
* [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - a suite of advanced algorithms for model inference optimization including quantization, filter pruning, binarization and sparsity
* [OpenVINO™ Training Extensions (OTE)](https://github.com/openvinotoolkit/training_extensions) - convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
* [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving deep learning models optimized for Intel architectures
* [Computer Vision Annotation Tool (CVAT)](https://github.com/opencv/cvat) - an online, interactive video and image annotation tool for computer vision purposes.
* [Dataset Management Framework (Datumaro)](https://github.com/openvinotoolkit/datumaro) - a framework and CLI tool to build, transform, and analyze datasets.
