Making Computer Vision Real Today – For Any Application

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With the demand for intelligent vision
solutions increasing everywhere from edge to cloud, enterprises of every type
are demanding visually-enabled – and intelligent – applications for
surveillance, retail, manufacturing, smart cities and homes, office automation,
autonomous driving, and more coming every day. Increasingly, AI applications
are powered by smart vision inputs.

Up till now, most intelligent computer
vision applications have required a wealth of machine learning, deep learning,
and data science knowledge to enable simple object recognition, much less
facial recognition or collision avoidance. That’s all changed with the
introduction of Intel’s
Distribution of OpenVINO
Visual Inference and Neural Network Optimization) toolkit.

With deep learning revenue expected to grow
to $35 billion by 2025, the need for accelerating deployment is clear. Here’s
some of the reasons to download and use this new intel toolkit

OpenVINO includes
Intel’s deep learning deployment toolkit, which includes a model optimizer that
imports and trains models from a number of frameworks (Caffe, Tensoflow, MxNet,
ONNX, Kaiai), optimizes topologies, and provides a huge performance boost by
conversion to data types that match hardware types – whether code is running on
CPUs, GPUs, VPUs, or FPGAs – or any combination of them. This fast,
heterogeneous performance is proven to yield up to 19x performance gain
compared to public deep learning models.

also includes a host of samples for image classification and segmentation,
object detection, neural style transfer, face detection, people counting, among
others, and dozens of pre-trained models for everything from age and gender to crossroad
object detection to vehicle metadata.

Optimized libraries in the package include
OpenCV – a popular open-source computer vision library with a broad range of
algorithms and functions and OpenVX – an optimized, graph-based approach for
computer vision functions targeted at real-time, low-power apps.

Also included in this distribution are the Intel Media SDK
to speed medic encode/decode, and users can work with the Intel OpenCL
drivers and runtime to assist in creation of custom kernels.

With OpenVINO, developers can

  • Boost computer vision
  • Streamline deep learning
    inference and deployment
  • Speed development for vision
  • Save time by taking a
    heterogeneous approach to vision processing

With support for popular OSs including
CentOS, Ubuntu, Windows, and Yocto Project, the OpenVINO toolkit gives every
vision-enabled project a powerful enhancement. Intel offers OpenVINO free
of charge to help developers get the most out of their Intel hardware.

Download the Intel OpenVINO toolkit today here


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