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£ 5.640 Million

Neurotechnology launches new platform to create AI-based image recognition models

Biometrics and deep learning company Neurotechnology today announced the launch of a, a new platform to let users develop their own deep learning models for image classification without having to write any code.

The web platform for AI-based image recognition applications. Credit: Neurotechnology website.

The Vilnius-based firm said the AI models produces through can be used in a variety of industries, from e-commerce to agriculture, medicine and beyond.

The program is able to perform several image recognition tasks, such as moderating user, tagging images with visible ads, detecting damaged products, classifying x-ray photos and much more.

“With, you can quickly and easily implement modern deep learning techniques to build an AI model and immediately start identifying content in images,” said Dr Karolis Uziela, team lead from Neurotechnology. “It enables users to manage a tremendous number of images in an easy and efficient way.”

Coming as an update of SentiSight SDK in Neurotechnology’s product line, uses cutting-edge, deep learning algorithms that update the previous version’s more traditional approach to image recognition.

The old SentiSight SDK could only recognise particular learned objects and not object classes. Next-generation is able to identify classes of objects that are more abstract and can differ quite a lot from each other.

For a more detailed insight about you can read about its features down below or visit the company’s site here.

  • Image labelling toolkit: The process of data labelling is very important, but it can also be time-consuming. tools make this work easier and a lot more efficient. Image labelling is done using an intuitive interface and the output labels are automatically saved in a correct format that is suitable for deep learning algorithms.
  • Interactive model training environment. Interactive model training provides the ability to track training processes and explore the results. produces statistics—prediction accuracy, precision, recall, F1 score and many others—that allow users to measure their models’ performance. Users can immediately view, filter and download the predictions. Trained models can either be used online inside the platform or via REST API.
  • Custom projects. For those who have a large and complicated project or just need a little more help, Neurotechnology experts can assist with the workflow.

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