Neurotechnology Releases New Version of SentiSight.ai Image Labeling and Recognition Platform Powered by Deep Learning

VILNIUS, Lithuania, Aug. 17, 2020 /PRNewswire/ -- Neurotechnology, a provider of deep learning-based solutions and high-precision biometric identification technologies, today announced the release of a new version of its SentiSight.ai image labeling and recognition platform. The new release includes some additional features and an improved interface for free platform users. It also includes a number of powerful features, such as object detection model training, offline models, project sharing and labeling time-tracking, which will be available for paid users.

SentiSight.ai is a web-based platform that can be used for image labeling and for developing AI-based image recognition applications. It has two major goals: the first is to make the image annotation task as convenient and efficient as possible, even for large projects with many people working on image labeling, and the second is to provide a smooth and user-friendly interface for training and deploying deep neural network models. The ability to perform both of these tasks on the same platform provides the advantage of being able to label images and then train and improve models in an iterative way.

"SentiSight.ai has become a platform of choice for image labeling and almost any AI-related task," said Dr. Karolis Uziela, SentiSight.ai team lead from Neurotechnology. "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 SentiSight.ai platform was first released as a free-to-use tool on November 19, 2018. Since then, a number of new features have been added to the free version:

    --  Improved image annotation tool. The original version of SentiSight.ai
        only allowed adding classification labels, bounding boxes and polygons.
        Now users can also label points, polylines and bitmaps. Bitmap labeling
        speed can also be significantly increased by using the smart labeling
        tool, 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 SentiSight.ai
        platform, or they can be downloaded and used for in-house model
        training.

    --  Pre-trained models. Originally, SentiSight.ai 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 several
        tasks, such as content moderation, goods classification, automatic
        hashtags, people counting and more.

    --  Similarity search. This new ready-to-use SentiSight.ai 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.

In addition to the above features that are available for all customers, the new version of SentiSight.ai has several features that will be available for paid customers:

    --  Object detection model training. Previously SentiSight.ai offered only
        classification model training. These types of models can be used to
        identify what is inside the image as a whole. Now SentiSight.ai also
        offers 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
        SentiSight.ai, the image recognition models could be used either via
        REST API or web interface. Both of these options required internet
        connection. The new SentiSight.ai offers a third option: 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. The price of the license depends on the speed of the
        model, and it is a single time payment.

    --  Shared labeling projects and time tracking. To make large annotation
        project handling easier, SentiSight.ai allows a project to be shared
        among multiple users so that multiple people can label images in the
        same project. The project manager can quickly 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.

For more information about SentiSight.ai, please visit www.sentisight.ai.

ABOUT NEUROTECHNOLOGY

Neurotechnology is a developer of high-precision algorithms and software based on deep neural networks and other AI-related technologies. The company was launched in 1990 in Vilnius, Lithuania, with the key idea of using neural networks for various applications, such as biometric person identification, computer vision, robotics and artificial intelligence. Since the first release of its fingerprint identification system in 1991, the company has delivered more than 200 products and version upgrades. More than 3,000 system integrators, security companies and hardware providers in more than 140 countries integrate Neurotechnology's algorithms into their products. The company's algorithms have achieved top results in independent technology evaluations, including NIST MINEX, PFT, FRVT, IREX and FVC-onGoing.

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SOURCE Neurotechnology