OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Rafal Blaczkowski e4875f2a6b
Add MSFT models to OpenVino ONNX CI check (#2377)
* Add MSFT models to OpenVino ONNX CI and update xfails

* Update paths

* Fix typo and update xfails

* Unset xfails to check current behavior

* Update:
* add MSFT models to preprocesing script
* update xfail names

* Final update of xfail test cases

* Update xfail paths

* Update xfails

* Uncomment part of preprocesing script

* Update script

* Add cleaning support for MSFT preprocesing

* Add -e option to script help

* Initilze variables

* Update ngraph/python/tests/test_onnx/model_zoo_preprocess.sh

Fix a mistake

Co-authored-by: Tomasz Socha <tomasz.socha@intel.com>

* Update ngraph/python/tests/test_onnx/model_zoo_preprocess.sh

align spaces

Co-authored-by: Tomasz Socha <tomasz.socha@intel.com>

Co-authored-by: Tomasz Socha <tomasz.socha@intel.com>
2020-09-29 19:05:31 +03:00
.ci/openvino-onnx Add MSFT models to OpenVino ONNX CI check (#2377) 2020-09-29 19:05:31 +03:00
.github [JAVA] Code style check added (#1984) 2020-09-09 17:49:23 +03:00
cmake [CMAKE] Introduce FASTER_BUILD experimental feature (#2438) 2020-09-28 18:53:11 +03:00
docs Fixed docs build on Windows (#2398) 2020-09-24 12:13:27 +03:00
inference-engine [GNA] clear input scale factor from configuration for imported model (#2172) 2020-09-29 18:32:09 +03:00
model-optimizer [MO] Add explicit broadcasting mode (#2077) 2020-09-25 13:26:47 +03:00
ngraph Add MSFT models to OpenVino ONNX CI check (#2377) 2020-09-29 19:05:31 +03:00
openvino Bump cmake version to 3.13 (#2258) 2020-09-18 18:58:12 +03:00
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tests Add fill_inputs step in timetests (#2413) 2020-09-25 14:08:03 +03:00
tools Adds first inference time measurements in benchmark_app (#1487) 2020-07-27 16:45:07 +03:00
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install_dependencies.sh [Docs] Fixes in readme files: (#750) 2020-06-03 20:14:35 +03:00
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 Azure DevOps builds (branch)

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

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:


* Other names and brands may be claimed as the property of others.