ed80e2eee8
* Fixed Model Optimization Guide and NNCF docs * Fixed the link to Optimum * Updated installatin guide * Changed API description * Changes quantization documents * Fixed links in the relevant components * Fixed API description * Revised CLI document * Fixed formatting bugs in the main document * Fixed formatting bugs in the main document * Changed the structure. Added Default quantization usage via API * Fixed E2E CLI example * Added AccuracyAware usage description * Revised structure and examples * Fixed a link to POT intro * Changed the structure for algorithms * Fixed links * Additional fixed of the links * Revised Ranger documentation * Some fixes * Revised Best Practicies * Fixed descriptions * Fixed section names * Changed the workflow one more time * Additional fixes to the model structure * Fixed AA usage * Added DefaultQuantization flow image * Fixed many issues * Fixed many issues * Applied many comments * Additional fixes * Fixed examples and provided links to them * Changed DataLoader Example. Fixed FAQ * Changed the main README for GitHub * Fixed E2E CLI example * Fixed links and code of DataLoader * Fixed build issues * Fixed more links * Fixed one more documentation build issue * Fixed more links * Fixed code example * Add multiple data loaders * Add audio example * Minor fixes in the code of sample loaders * Add descriptions of dataloaders. Changed the behaviour of text loader * Fixed typos * Added a new item into the FAQ * Apply wording corrections * Update docs/OV_Runtime_UG/supported_plugins/CPU.md Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> * Fixed comments Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
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OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.
This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
Repository components
License
OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
Resources
- Docs: https://docs.openvino.ai/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ toolkit modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.