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Sample Applications

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These catalog contains sample applications that fulfill specific market needs and examples to optimize, tune and accelerate applications to run efficiently on Intel architectures.

Accelerated Object Detection sample icon

Accelerated Object Detection

Basics

Learn how to accelerate your object detection applications with Asynchronous inference and offloading to multiple types of processing units.

Store Aisle Monitor sample icon

Store Aisle Monitor

Retail

Capture video, generate a heat map, record the number of patrons, then integrate the results. The program can also create output video and save snapshots.

Store Traffic Monitor sample icon

Store Traffic Monitor

Retail

The Store Traffic Monitor builds upon the two previous examples to provide a more advanced use case. This reference sample demonstrates how to perform object detection and inference with multiple videos simultaneously. In this use case, the application monitors the activity of people inside and outside an imaginary storefront and keeps track of product inventory using a pretrained neutral network for detection.

Shopper Gaze Monitor sample icon

Shopper Gaze Monitor

Retail

Build a solution to analyze customer expressions and reactions to product advertising positioned on retail shelves.

People Counter System sample icon

People Counter System

Transportation | Security

Create a smart video application using the Intel® Distribution of OpenVINO™ toolkit. The toolkit uses models and inference to run single class object detection.

Pneumonia Detection sample icon

Pneumonia Detection

Healthcare

This example showcases a healthcare application by classifying the probability of pneumonia in X-ray images. The application uses the inference engine in the Intel® Distribution of OpenVINO™ toolkit and applies a pretrained neural network using an open source dataset. The inference results are stored in an output file.

Brain Tumor Segmentation sample icon

Brain Tumor Segmentation

Healthcare

This example implements an inference engine based on the U-Net architecture for brain tumor segmentation in MRI scans. The code demonstrates several approaches. First, a stock TensorFlow implementation is presented. Next, the same implementation is executed with an Intel-optimized TensorFlow backed by MKL-DNN. We also present an alternative implementation that relies on the Intel® Distribution of OpenVINO™ toolkit. This latter implementation allows you to use not only Intel CPUs for inference, but also Intel HD Graphics, Intel Neural Compute Stick 2, HDDL-R, and Intel FPGAs (HDDL-F).

Safety Gear Detection sample icon

Safety Gear Detection

Industrial | Security

The Safety Gear Detection sample is another demonstration of object detection, this time in an industrial/safety use case. The sample involves presenting a video frame-by-frame to the inference engine (IE), which then uses a trained and optimized neural network – Mobilenet-SSD – to detect people and their safety gear.

Optical Character Recognition sample icon

Optical Character Recognition

Basics

The Optical Character Recognition (OCR) sample demonstrates the use of the Intel® Distribution of OpenVINO™ toolkit to perform OCR using Long Short-Term Memory (LSTM), which is a Convolutional Recurrent Neural Network (CRNN) architecture for deep learning. The sample recognizes words in a sample JPEG file.

Intruder Detection sample icon

Intruder Detection

Security

The Intruder Detection sample demonstrates how to build an application to alert you when someone enters a restricted area. Here, you will learn how to use models to perform multiclass object detection.

Accelerated Object Detection (C++) sample icon

Accelerated Object Detection (C++)

Basics | General

Learn how to accelerate your object detection applications with asynchronous inference and offloading to multiple types of processing units.

New Speech Recognition C++ Sample sample icon

New Speech Recognition C++ Sample

General

This sample demonstrates the building of speech recognition application using a Kaldi acoustic and language model to transcribe an audio file. The Intel® Distribution of OpenVINO™ toolkit inference engine (IE), Intel® Speech Decoder, and Intel® Speech Extraction libraries are used to create a simple Speech Library API.

American Sign Language (ASL) Recognition sample icon

American Sign Language (ASL) Recognition

General

The ASL Recognition sample demonstrates how to use a person detection model to track a person, plus a gesture recognition model to identify up to 100 ASL gestures used by the tracked person. The application uses the Intel® Distribution of OpenVINO™ toolkit inference engine (IE) and renders a bounding box with a text translation of gestures attached to each frame.

Coming soon

Clean Room Worker Safety - ONNX Object Detection Sample sample icon

Clean Room Worker Safety - ONNX Object Detection Sample

Industrial | Security

The Clean Room Worker Safety sample demonstrates object detection in an industrial/clean room use case. The sample involves presenting a frame-by-frame video to the ONNX Runtime (RT), which uses an ONNX RT Execution Provider for OpenVINO™ toolkit to run inference on various Intel® hardware, such as CPU, iGPU, accelerator cards NCS2, FPGA, and VAD-M. This sample uses a pretrained Tiny Yolo V2 Deep Learning ONNX Model for the detection of safety gear (e.g., bunny suit, safety glasses), robots, and heads, and can be used for hazard detection purposes.

Radar Image Classification sample icon

Radar Image Classification

Public Sector/Government

This example showcases the use of Intel® Distribution of OpenVINO™ Toolkit to optimize and deploy a pre-trained ResNet18 model that classifies Synthetic Aperture Radar (SAR) images associated with 10 separate military vehicle classifiers, such as tanks and armored vehicles.

Tiny Yolo V3 Object Detection sample icon

Tiny Yolo V3 Object Detection

General

This sample showcases how to convert a pre-trained DarkNet Tiny YOLO V3 model to TensorFlow format, run accelerated Inference with Tiny YOLO V3 using OpenVINO's Inference engine, and fine tune the number of inference request processed at a time and the number of streams to achieve optimal performance.

Robotic Surgery Segmentation sample icon

Robotic Surgery Segmentation

Healthcare

This sample showcases a Pytorch model based on TeranusNet being converted to ONNX to utilize Intel® Distribution of OpenVINO™ toolkit for accelerated inference to segment robotic tools in an endoscopic surgical video. The PyTorch model won the 2017 MICCAI Robotic Surgery Instrument Segmentation Challenge.

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Roll up your sleeves with our step-by-step instructions on using computer vision at the edge with these advanced Jupyter* Notebook tutorials.