Pretrained Models

The Intel® Distribution of OpenVINO™ toolkit includes two sets of optimized models that can expedite development and improve image processing pipelines for Intel® processors. Use these models for development and production deployment without the need to search for or to train your own models.

Age & Gender Recognition

This neural network-based model provides age and gender estimates with enough accuracy to help you focus your marketing efforts.

Text Detection

This model is based on PixelNet* architecture with MobileNetV2* as a backbone. It enables the ability to detect text in indoor and outdoor scenes.


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Attention-Based Approach

Enhance the input image resolution by a factor of four or three with single-image, super resolution networks that are built on this approach. The two models are faster than the SRResNet-based networks and have better memory consumption.


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Standard Model

Identify faces for a variety of uses, such as observing if passengers are in a vehicle or counting indoor pedestrian traffic. Combine it with a person detector to identify who is coming and going.

Facial Landmarks Detection

This is a custom architecture based on a convolution neural network. It detects 35 facial landmarks that cover eyes, noses, mouths, eyebrows, and facial contours.

Lightweight Facial Landmarks Detection

This lightweight regressor model identifies five facial landmarks: two eyes, a nose, and two lip corners. The model is best suited for smart classroom use cases.

Face Reidentification 

Use this lightweight network for face reidentification in smart classroom scenarios. For best results, use a frontally oriented and aligned input face.

High-Angle Detection

Use this model for cameras mounted at higher vantage points to count the people in a frame.

People, Vehicles & Bikes Detection

Distinguish between people, people riding bikes, bikes alone, and vehicles. A variety of lighting conditions in this model improve accuracy in daylight, darkness, and variations in the weather.

Pedestrian Detection

Distinguish between people and objects in public using a network that is based on an SSD framework with a tuned MobileNetV1* as a feature extractor.

Pedestrian & Vehicle Detection

Identify people and vehicles by using a network that is based on an SSD framework with a tuned MobileNetV1 as a feature extractor.

Pedestrian Attributes

Identify key attributes of a person crossing the road: gender, hat, long sleeves, long pants, long hair, coat, and jacket.

Action Detection for a Smart Classroom

This model recognizes poses that include sitting, standing, and raising a hand. Use this action detector for a smart classroom scenario based on the RMNet backbone with depthwise convolutions.

Human Pose Estimation

This multiperson, 2D pose estimation network is based on the OpenPose approach and uses a tuned MobileNetV1 to extract features. It detects a skeleton (which consists of keypoints and connections between them) to identify human poses for every person inside the image. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles.

Vehicle Detection

Identify vehicles by applying an SSD framework that uses a tuned MobileNetV1 to extract features.

License Plate Detection: Small-Footprint Network

This model has a small-footprint network that's trained to recognize (but not read) a variety of Chinese license plates.

License Plate Detection: Front-Facing Camera

This MobileNetV2 and SSD-based vehicle and license plate detector recognizes Chinese license plates from a front-facing camera. This model is useful for security barriers that require front license plate detection.


Note This model replaces the previous version and runs faster while maintaining the same accuracy.

Vehicle Metadata

Conduct an initial analysis and present back-key metadata for faster sorting and searching in the future. The average color accuracy for the model is over 82 percent for red, white, black, green, yellow, gray, and blue. Its average vehicle-type attribution is over 87 percent for cars, vans, trucks, and buses.

Identify Roadside Objects

Classify objects as roads, curbs, painted lines, or backgrounds.

Advanced Roadside Identification

Classify objects as roads, sidewalks, buildings, walls, fences, poles, traffic lights, traffic signs, vegetation, terrain, sky, people, passengers, cars, trucks, buses, trains, motorcycles, bicycles, or electric vehicles.

Product and Performance Information


Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804