5e023ebdd9
* Fix in Preprocessing python bindings - add correct default arguments for:
- PreProcessSteps::convert_element_type
- PostProcessSteps::convert_element_type
- InputTensorInfo::set_color_format
Otherwise, python users must always specify optional params
E.g. instead of writing `tensor().set_color_format(ColorFormat.RGB)` python users will have to write `tensor().set_color_format(ColorFormat.RGB, [])`
* Corrected 'help' output
* Exposing 'openvino.runtime.Type.undefined' and use it in 'convert_element_type' documentation
OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.
This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, 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 TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
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.openvino.ai/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ toolkit 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.