19ace232cf
* refactor: update ie python samples * python samples: change comment about infer request creation (step 5) * python sample: add the ability to run object_detection_sample_ssd.py with a model with 2 outputs * Add batch size usage to python style transfer sample * Change comment about model reading * Add output queue to classification async sample * add reshape for output to catch results with more than 2 dimensions (classification samples) * Set a log output stream to stdout to pass the hello query device test * Add comments to the hello query device sample * Set sys.stdout as a logging stream for all python IE samples * Add batch size usage to ngraph_function_creation_sample * Return the ability to read an image from a ubyte file * Add few comments and function docstrings * Restore IE python classification samples output * Add --original_size arg for python style transfer sample * Change log message to pass tests (object detection ie python sample) * Return python shebang * Add comment about a probs array sorting using np.argsort * Fix the hello query python sample (Ticket: 52937) * Add color inversion for light images for correct predictions * Add few log messages to the python device query sample |
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| inference-engine | ||
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| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| thirdparty | ||
| tools | ||
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| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
OpenVINO™ Toolkit
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
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit 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.