ca6f8fc9cf
* Updated MO extension guide * Minor change and adding svg images * Added additional information about operation extractors. Fixed links and markdown issues * Added missing file with information about Caffe Python layers and image for MO transformations dependencies graph * Added section with common graph transformations attributes and diagram with anchor transformations. Added list of available front phase transformations * Added description of front-phase transformations except the scope-defined and points defined. Removed legacy document and examples for such transformations. * Added sections about node name pattern defined front phase transformations. Copy-pasted the old one for the points defined front transformation * Added description of the rest of front transformations and and all middle and back phase transformations * Refactored Legacy_Mode_for_Caffe_Custom_Layers and updated the Customize_Model_Optimizer with information about extractors order * Added TOC for the MO Dev guide document and updated SVG images with PNG ones * Fixed broken link. Removed redundant image * Fixed broken links * Added information about attributes 'run_not_recursively', 'force_clean_up' and 'force_shape_inference' of the transformation * Code review comments * Added a section about `Port`s * Extended Ports description with examples * Added information about Connections * Updated MO README.md and removed a lot of redundant and misleading information * Updates to the Customize_Model_Optimizer.md * More updates to the Customize_Model_Optimizer.md * Final updates for the Customize_Model_Optimizer.md * Fixed some broken links * More fixed links * Refactored Custom Layers Guide: removed legacy and incorrect text, added up-to-date. * Draft implementation of the Custom layer guide example for the MO part * Fixed broken links using #. Change layer->operation in extensibility documents * Updated Custom operation guide with IE part * Fixed broken links and minor updates to the Custom Operations Guide * Updating links * Layer->Operation
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
This open source version includes several components: namely Model Optimizer, ngraph and Inference Engine, 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 Caffe*, TensorFlow*, MXNet* and ONNX*.
Repository components:
License
Deep Learning Deployment 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.openvinotoolkit.org/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ 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.