These advanced instructional guides provide step-by-step coding exercises to help you quickly learn how to build computer vision applications.
Migrate to Local Edge
This step-by-step tutorial demonstrates how to migrate from DevCloud to local edge device(s).
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Migrate from Edge Device
This step-by-step tutorial demonstrates how to migrate from an edge device to DevCloud.
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Benchmark App
This tutorial demonstrates how to use the benchmark app to estimate inference performance of your deep learning model on various devices.
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DL Streamer
These tutorials walk you through the process of building a modular GStreamer pipeline to perform object detection, tracking, and classification using the DL Streamer component of OpenVINO™ toolkit.
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Post-Training Optimization
Demonstrates how to use Intel® Distribution of OpenVINO™ toolkit to quantize a sample model from 32-bits to 8-bits precision on CPU architectures.
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Accelerated Object Detection with Encrypted Model (Python)
This tutorial demonstrates how to use Intel® Distribution of OpenVINO™ Toolkit securely with protected models. This tutorial has two sections, first section takes you through the steps on how to optimize a model and encrypt it before deploying it to the edge device (DevCloud). The other section shows how the encrypted model is decrypted in runtime only for use by the Inference Engine while protecting it during transit and at rest. This tutorial uses a simple object detection application but could be applied for other use cases too.
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