da47cb05be
* Commit. * Written draft of NonMaxSuppression-5 class. * Written conversion of the value of the second output of MO NonMaxSuppression-5 into TF format. * Fixed type infer for the port 1 of NonMaxSuppression-5. * Added Reshape to [1] for 0D inputs of NMS-5. * Small fix. * Corrected assert for number of inputs. * Fixed docstrings for transformations TFNonMaxSuppressionNormalize and NonMaxSuppressionNormalize. * Now the transformation TFNonMaxSuppressionNormalize uses find_and_replace_pattern(). * Moved model-optimizer/extensions/front/onnx/non_max_suppression_normalize.py to model-optimizer/extensions/front/non_max_suppression_normalize.py, to delete duplicate code. * Deleted commented code. * Fixed BOM-file. * Deleted out_ports_count from NMS. * Fixes in type_infer of NMS-5. * Small changes. * Added some comment. * Small fix. * Some fixes.
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 two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU 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.
Documentation
- OpenVINO™ Release Notes
- OpenVINO™ Inference Engine Build Instructions
- Get Started with Deep Learning Deployment Toolkit on Linux*
- Introduction to Deep Learning Deployment Toolkit
- Inference Engine Developer Guide
- Model Optimizer Developer Guide
- Get Started with DockerHub CI for OpenVINO™ toolkit
How to Contribute
See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!
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.