Deep Learning Powers Image Labeling and Recognition Platform

Neurotechnology, a provider of deep learning-based solutions and biometric identification technologies, has released an updated version of its image labeling and recognition platform. The free edition comes with an improved user interface; the paid-subscription edition gets the new interface plus a slew of other features, including new object detection model training, offline models, project sharing, and labeling time-tracking. is a web-based platform that can be used for image labeling and developing AI-based image recognition applications. The platform, which was originally released as a free-to-use tool in 2018, allows users to label specific objects and events, and then train the system to spot other instances of that image type in a larger database.

" has become a platform of choice for image labeling and almost any AI-related task," said team lead Karolis Uziela, in a statement. "It has also become one of the first such platforms to offer the ability to download offline models, which allows our clients to be completely independent both from the platform and from their connection to the internet."

The list of new features added to the free version includes:

  • Improved image annotation tool. The original version of allowed adding classification labels, bounding boxes, and polygons only. Now users can label points, polylines, and bitmaps. Bitmap labeling speed can also be significantly increased by using the smart labeling tool, the company says, which allows users to select a few points in the foreground and the background and let the AI extract the labeled object. The labeled images can be directly used for model training on the platform, or they can be downloaded and used for in-house model training.
  • Pre-trained models. Originally, focused on providing a user interface for custom model training. Now it also provides several pre-trained models that can be used out-of-the-box without any additional training. These pre-trained models can be used for a number of tasks, such as content moderation, goods classification, automatic hashtags, and people counting, among others.
  • Similarity search. This new ready-to-use feature allows users to upload an image and find all similar images to this query in their data set. It also allows users to perform NvN similarity searches in their data set where all similar image pairs are retrieved.

Users of the paid edition get all of the above, plus:

  • Object detection model training. Previously offered only classification model training. These types of models can be used to identify what is inside the image as a whole. Now the platform provides object detection model training. This type of model can not only identify objects in an image, but also predict their exact location.
  • Offline models (free 30-day trial available). In the previous version of platform, the image recognition models could be used either via a REST API or Web interface. Both of these options required an Internet connection. The new version allows users to download and use the image recognition model offline. An offline model can be downloaded as a free 30-day trial, after which the user has an option to buy a license.
  • Shared labeling projects and time tracking. To make large annotation project handling easier, the platform allows a project to be shared among multiple users, so that multiple people can label images in the same project. The project manager can filter and review the images labeled by a particular project member, track each person's progress, and time spent on labeling, as well as manage user roles and permissions.

Founded in 1990, Neurotechnology is based in Vilnius, Lithuania.

About the Author

John K. Waters is the editor in chief of a number of sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS.  He can be reached at