Defect Detection with Anomalib Edge AI Reference Kit | Intel Software
24An introduction to how our Edge AI Reference Kit’s Defect Detection with Anomalib application works in a few simple steps. Showcases the positive impact it can have on quality control in manufacturing, healthcare, agriculture, and more. Optionally, this application even incorporates usage of a Dobot.
Intel’s Edge AI Reference Kits are ready-made with prebuilt components and pretrained models to accelerate your AI solution development. The kit in this video focuses on Defect Detection with Anomalib, an end-to-end AI solution that detects defects in real time. Anomalib is an open source library that enables unsupervised anomaly detection, a method that allows usage of imbalanced datasets. In real world situations, there are many rare or unknown defects that can occur, which cannot be handled by supervised learning.
Using computer vision, object detection, defect detection, and optionally a Dobot robot, Defect Detection with Anomalib goes through training, testing, optimization, and deployment to a variety of Intel hardware. It enables faster, more accurate detection of defects, subsequently improving security and operational efficiency.
OpenVINO is an open source toolkit that accelerates AI inference on a wide variety of hardware, including CPUs and GPUs, and powers our Edge AI Reference Kits.
Watch the code demo walkthrough video: https://youtu.be/OifcJbZRaGM
Our Github with Jupyter Notebook: https://bit.ly/3ZsEvvf
Download the Intel Distribution of OpenVINO Toolkit: https://intel.ly/3JAW716
OpenVINO 2023.0 — OpenVINO™ documentation: https://bit.ly/3Yyv1xX
OpenVINO™ Toolkit Medium: https://medium.com/openvino-toolkit
Learn more about our Edge AI Reference Kits: https://intel.ly/3YG3Q4t
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Defect Detection with Anomalib Edge AI Reference Kit | Intel Software