OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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[IE CLDNN] Extend resample int8 packing optimization (#1662)
This extends resample optimization for 8-bit types that uses feature
packed to mode to process multiple features in one work-item to features
not being multiple of packing factor.

For nearest resampling it is safe to copy extra feature padding for
blocked formats, so this change only removes this condition.
2020-08-07 16:08:40 +03:00
.ci/openvino-onnx ONNX CI: add docker image cleanup (#1394) 2020-07-20 13:42:52 +03:00
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cmake ENABLE_PROFILING_ITT option is ignored if ITT library not found (#1647) 2020-08-06 13:20:35 +03:00
docs docs contribution guides (#1535) 2020-08-07 15:33:11 +03:00
inference-engine [IE CLDNN] Extend resample int8 packing optimization (#1662) 2020-08-07 16:08:40 +03:00
model-optimizer Add mxnet extractors (#1667) 2020-08-07 14:36:41 +03:00
ngraph Moved frontends to separate folder (#1657) 2020-08-07 13:08:38 +03:00
openvino ENABLE_PROFILING_ITT option is ignored if ITT library not found (#1647) 2020-08-06 13:20:35 +03:00
scripts setupvars.sh: Updated logic for detecting INSTALLDIR - using relative path every time instead of checking <INSTALLDIR> or INTEL_OPENVINO_DIR (#1536) 2020-07-29 18:23:36 +03:00
tests [Stress] Define --timeout in run_memcheck.py used in gtest-parallel (#1576) 2020-08-05 12:33:01 +03:00
tools Adds first inference time measurements in benchmark_app (#1487) 2020-07-27 16:45:07 +03:00
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0

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.