Overview of Intel® Distribution of OpenVINO™ Toolkit
AI inference applies capabilities learned after training a neural network to yield results. The Intel® Distribution of OpenVINO™ toolkit enables you to optimize, tune, and run comprehensive AI inference using the included model optimizer and runtime and development tools.
Discover the Capabilities
High Performance, Deep Learning
Convert and optimize models to achieve high performance for deep-learning inference applications.
Streamlined Development
Facilitate a smoother development process using the included inference tools for low-precision optimization and media processing, computer vision libraries, and preoptimized kernels.
Write Once, Deploy Anywhere
Deploy your same application across combinations of host processors, accelerators, and environments, including CPUs, GPUs, VPUs, FPGAs, on-premise and on-device, and in the browser or in the cloud.
How It Works
Use the Open Model Zoo to find open-source, pretrained, and preoptimized models ready for inference, or use your own deep-learning model.
Run the trained model through the Model Optimizer to convert the model to an Intermediate Representation (IR), which is represented in a pair of files (.xml and .bin). These files describe the network topology and contain the weights and biases binary data of the model.
Use the Inference Engine to run inference and output results on multiple processors, accelerators, and environments with a write once, deploy anywhere efficiency.
What You Can Do
See how developers use the Intel Distribution of OpenVINO toolkit on multiple Intel® architectures to enable new and enhanced use cases across industries, including manufacturing, health and life sciences, retail, security, and more.
What's New in the 2021.4.1 LTS Release
- Learn more about included components in the Release Notes. This update includes:
- Specific fixes to known issues with Model Optimizer, Inference Engine (plug-ins for Inference Engine Python* API, CPU, GPU, Intel® Movidius™ Myriad™ VPU, HDDL, and Intel® Gaussian & Neural Accelerator), Deep-Learning Streamer, and Post-Training Optimization Tool
- Minor capability changes and bug fixes to the Open Model Zoo
- New Jupyter* Notebook tutorials simplify how to get started:
- Detect text and perform scene-text recognition: Optical Character Recognition
- Demonstrate a post-training INT8 quantization pipeline for NLP models: HuggingFace BERT Quantization Tutorial
- Show basic object detection using a text-detection model: Hello Detection
- Demonstrate basic semantic segmentation with a road segmentation model: Hello Segmentation
- Convert PaddlePaddle* models to the Intel Distribution of OpenVINO toolkit format: Image Classification and PaddleGAN Super Resolution
Community and Support
Explore different ways to get involved and stay up-to-date with the latest announcements.
Awarded by the Embedded Vision Alliance*
The productive, smart path to freedom for accelerated computing from the economic and technical burdens of proprietary alternatives.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.