![]() Added three new pre-trained models for vision:.Ubuntu 16.04 is still supported, but with reduced validation scope. Added support for Ubuntu* 18.04 as a primary platform for developers.This is useful when shape size changes between inferences. Improved performance through network loading optimizations and sped inference by reducing model loading time.For more details, see Introduction to CLI Deployment Manager. It makes the deployment footprint several times smaller than the development footprint. With this tool, the Inference Engine can be deployed with pre-compiled application-specific data, such as models, configuration, and a subset of required hardware plugins. Introduced a new Command Line Deployment Manager tool to help you generate the optimal, minimized runtime package for your selected target device.Easily deploy inference applications optimized by Intel® Distribution of OpenVINO™ toolkit on these processors with minimal to no code changes and achieve high performance. ![]() They include a GPU, as well as Intel® Gaussian & Neural Accelerator (Intel® GNA) for offloading critical workloads. Added support for 10th generation Intel® Core™ processors, which are purpose-built for accelerating AI workloads through Intel® Deep Learning Boost.Users should update to the latest version. Intel® Distribution of OpenVINO™ Toolkit 2019 R3 includes functional and security updates.New and Changed in the Release 3 Executive Summary Added bitstreams for Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA (Intel® PAC with Intel® Arria® 10 GX FPGA).This significantly simplifies user journey for first inference experiments and decreases the learning curve while keeping all the necessary functionality for advanced users. The parameters are required only if you want to measure accuracy and/or do Int-8 calibration. Simplified the model import process in the Deep Learning Workbench (DL Workbench). The new flow does not require that you set parameters for accuracy measurement during the import step.The conversion step is simplified by the internal analysis of the provided model and suggests required Model Optimizer parameters (normalization, shapes, inputs). Now you can start using DL Workbench with an original pre-trained model and proceed to model profiling and optimization through an intuitively clear conversion step. Introduced Model Optimizer support within Deep Learning Workbench (DL Workbench).Intel® Distribution of OpenVINO™ Toolkit 2019 R3.1 includes bug fixes. Users should update to the latest version.New and Changed in the Release 3.1 Executive Summary Wide chars support for a library path in plugins.xml in Core object.Fix for Inference Engine exceptions handling on 3rd Generation Intel® Core™ Processors (formerly Ivy Bridge) system.Crash fix for extra memory usage for convolution 1x1 with strides.On replacing a system memory allocator to TBB Scalable Memory allocator, refer to libtbbmalloc_proxy.so is now a part of the OpenVINO™ binary package.Please, review content inside the /licensing folder for more details. IMPORTANT: By downloading and using these packages, you agree to the terms and conditions of the software license agreements located here. Packages are available in the 2019 R3.1 download record (Related downloads section) Intel® Distribution of OpenVINO™ Toolkit 2019 R3.2 includes updates and bug fixes. New and Changed in the Release 3.2 Executive summary Includes optimized calls for CV standards, including OpenCV*, OpenCL™, and OpenVX*.Speeds time-to-market through an easy-to-use library of CV functions and pre-optimized kernels.Supports heterogeneous execution across Intel CV accelerators, using a common API for the CPU, Intel® Integrated Graphics, Intel® Movidius™ Neural Compute Stick (NCS), Intel® Neural Compute Stick 2, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs and Intel® FPGAs.Enables CNN-based deep learning inference on the edge.The Intel® Distribution of OpenVINO™ toolkit: Based on Convolutional Neural Networks (CNNs), the toolkit extends CV workloads across Intel® hardware, maximizing performance. The Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that emulate human vision. NOTE: For the Release Notes for the 2018 version, refer to Release Notes for Intel® Distribution of OpenVINO™ Toolkit 2018.
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