OpenVINO 2021.4.2 LTS Release

OpenVINO 2021.4.2 LTSがリリースされました
https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

LTSのマイナーバージョンのリリースなのでバグフィクスがメインですが、AlderLakeサポートが進んでいるようですね。

Intelからのリリースを転記します

What’s New in the 2021.4.2 LTS Release

This release provides functional bug fixes and minor capability changes for the previous 2021.4.1 Long-Term Support (LTS) release, enabling developers to deploy applications powered by the Intel Distribution of OpenVINO toolkit with confidence. To learn more about the long-term support and maintenance, see the Policy. Note A new LTS version is released every year and is supported for two years (one year of bug fixes and two years of security patches). The LTS version is intended for developers taking the OpenVINO toolkit to production. For developers that prefer the latest features and leading performance, standard releases are recommended. Standard releases continue to be available three to four times a year. Learn more about included components in the Release Notes. This update includes:

Support for the 12th generation Intel® Core™ processor family that is built on the Intel 7 process with new performance hybrid architecture that delivers enhancements in multithreaded performance to handle compute-intensive workloads.

Learn More

Specific fixes, capability improvements, and support updates to known issues with:
Model Optimizer, specifically issues causing accuracy regression
Inference Engine (plug-ins for Inference Engine Python* API, C API, GPU, Intel® Movidius™ Myriad™ VPU, HDDL, and Intel® Gaussian & Neural Accelerator)
Added support for the 12th generation Intel® Core™ processor family that enables Intel® Gaussian & Neural Accelerator (Intel® GNA) 3.0 and Intel GNA generation with native 2D convolutions
Functional performance improvements to testing and accuracy, fixes to bugs that caused performance degradation for several models, fixed heap-use-after-free, and memory leaks
Minor capability changes and bug fixes to the Open Model Zoo, specifically issues that affected the Accuracy Checker in the Deep Learning Workbench

Additional Jupyter* Notebook tutorials:
Use your laptop and webcam to run demonstrations for object detection and human pose estimation
Apply 8-bit quantization with a neural network compression framework to optimize your Keras and TensorFlow or PyTorch models
Learn how to show live inference, and optimize and quantize a segmentation model
See all tutorials