* remove power op from layer creator * remove prelu op from layer creator * remove tile op from layer creator * remove relu op from layer creator * remove selu op from layer creator * remove softmax op from layer creator * remove tanh op from layer creator * remove split op from layer creator * remove reshape op from layer creator * remove reverse sequence op from layer creator * remove proposal op from layer creator * remove priorbox op from layer creator * remove roipooling op from layer creator * remove priorboxclustered op from layer creator * style fix * utility function to parse bool-containing strings * align priorbox scale_all_sizes parameter to specification * change location of getBoolStrParamAsIntStr function * align prelu creator to new constant op changes * adjust priorbox tests to align with scale_all_sizes default value * adjust priorbox python tests to align with scale_all_sizes default value * align priorboxclustered attributes initlialization to specification * fix checking wrong container's end iterator for opset name search * improve comment on roipooling parameters * Apply review suggestion 1 Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Apply review suggestion 2 Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * align priorbox step initial value to specification * align roipooling method attribute to specification * remove roipooling specific creator * align with review comments Co-authored-by: Ilya Churaev <ilyachur@gmail.com> |
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cmake | ||
docs | ||
inference-engine | ||
licensing | ||
model-optimizer | ||
ngraph | ||
openvino | ||
scripts | ||
tests | ||
tools | ||
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CMakeLists.txt | ||
CODEOWNERS | ||
install_build_dependencies.sh | ||
Jenkinsfile | ||
LICENSE | ||
README.md | ||
SECURITY.md |
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
openvino
tag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.