0a0d854284
Introduce vpu ngraph transformation that decomposes Mish layer with the following formula: mish(x) = x * tanh(ln(exp(x) + 1)). It is needed to fix the accuracy drop of Yolo-v4 public model that happened due to Mish implementation in the myriad plugin is not accurate enough. It is just a workaround and will be removed when Mish layer will be improved. map: 70.97% (ref: 70.96%) coco_precision: 73.50% (ref 73.49%) |
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| cmake | ||
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| inference-engine | ||
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| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| tools | ||
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| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
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| 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
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.