Recognizing the importance of high-quality datasets in AI model training, companies dedicate significant time and cost in data labeling. However, they still need a reliable method to confirm the quality of the resulting dataset.

To tackle this issue, INFINIQ offers a range of dataset quality analytics reports designed to validate the quality of dataset. These reports provide valuable insights into the performance of deep neural networks (DNN) by comparing the ground truth data with the DNN results. Key metrics, such as Intersection over Union (IoU), precision, recall, and F1 score, can be derived at the class or scene level, enabling customers to assess DNN performance effectively.

At the 2023 Autonomous Vehicles Technology Expo in Europe, INFINIQ showcased its dataset quality analytics report and received an overwhelmingly positive response, demonstrating the industry's demand for reliable dataset evaluation solutions.

INFINIQ has also captured customer attention through its web-based tool, which facilitates faster and easier sensor-fusion and 3D segmentation annotation. The tool significantly enhances the accuracy and efficiency of annotation processes which is crucial for the development of autonomous driving technology.

As a leading data anonymization solution provider in Korea, INFINIQ has showcased a real-time, large-scale data anonymization solution. Through the demonstration of blurring and replacing visitors' faces and vehicle license plates at the expo, INFINIQ recognized the growing need for diverse applications of image/video data containing personal information and captured the interest from the European customers.