Transform the way you annotate data with TagSquare, an intelligent auto-image annotator to keep up with labeling workforce requirements and subsequent time lost in identifying and labeling scattered media assets. It helps your business speed its iteration time, shrink manual hours, and navigate labeling fidelity by providing accurate results.
Creating novel labeled data for object detection, image classification, and inventory management is a time- and resource-intensive task. Many businesses are now on the lookout for a single, automated, high-fidelity, and AI based image annotation tool to simplify their data labeling needs. This tool also needs to be coupled with the ability to learn and model the annotation of hundreds or thousands of images processed or captured to automate manually-ridden processes when labeling.
But, many image annotation tools lack capabilities owing to an already complex architecture or build that takes time in segmenting images. This leads to inaccurate results and added complexity of other data-fitting integrations for the business.
TagSquare is a unique auto-image annotator designed to deliver auto-labeling at speed as well as train for accuracy. This single node is equipped to orchestrate end-to-end model productionalizing from auto-labeling to model training and further iterations.
By automating 80% of data labeling based on 20% manual labeling, this AI-powered tool supports machine learning and iterates rapidly for highly accurate results. It helps accelerate time-to-market, thereby enhancing customer experiences. Moreover, this platform is driven by InfoVision’s proprietary neural network that enables intelligent data labeling and transforms the user flow to be frictionless.
