Training Neural Networks Without AI Expertise and Creating Individual Inference Cameras

Name: Training Neural Networks Without AI Expertise and Creating Individual Inference Cameras
Brand: IDS Imaging Development Systems GmbH

IDS NXT ocean is an all-in-one solution that makes it particularly easy for users to get started with AI-based image processing. From camera hardware including a self-developed AI core to intuitive training software for creating individual artificial neural networks and support, everything comes from a single source. This is unique in the image processing market.

With the help of the IDS NXT lighthouse cloud software, even non-experts without prior knowledge of artificial intelligence or camera programming can train an AI classifier. As a web application, all functions and the required infrastructure are immediately available. Users can start training their own neural network right away in three basic steps: Upload sample images, label them, and finally start the fully automatic training. The generated network can then be executed directly on IDS NXT industrial cameras, turning them into powerful inference cameras.

Inference Cameras for Industrial Use

IDS NXT inference cameras can apply their "knowledge" acquired through deep learning to new data. With a special AI core neural networks are hardware-accelerated and run directly on the devices – enabling inference times of just a few milliseconds. With features such as C-mount, robust housing, GigE network connection with RJ45, or M12 connectors, RS232 interface and REST web interface, the fully-fledged industrial cameras are also available with different sensors and protection classes.

Get Started with the Design-In Kit

All components a user needs to create, train and execute a neural network are offered by IDS as design-in kit. In addition to an IDS NXT industrial camera with 1.6 MP Sony sensor, lens and cable, the package includes six months of access to the AI training software. The use of deep learning-based image processing for individual applications can thus be realized in a short time.

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