f30656ed6d
* Now coordinate_transformation_mode used for all axes in the 'nearest' mode. * Temporarily added tests for Interpolate-4 evaluate(). * Deleted temporarily added tests. * Fixed documentation for the 'nearest' mode. * Small fixes. * Disabled Interpolate-4 layer tests for CPU. * Disabled some Interpolate-4 CPU tests. * do not change index table when execute each time * layout check added * interpolate for no batch size even scale is 1 * coordinate transformation with div scale, not multiple 1/scale, for higher accuracy * disable tests temporal * test modification * Some changes. * Enabled some tests. |
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| .github | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
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| tests | ||
| tools | ||
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| azure-pipelines.yml | ||
| build-instruction.md | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| CONTRIBUTING_DOCS.md | ||
| CONTRIBUTING.md | ||
| get-started-linux.md | ||
| install_dependencies.sh | ||
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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
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.