Voxel's Transformation: Enhancing In-house Labeling Operations for High-Quality Training Data

Voxel, a company leveraging AI and computer vision to manage risk and operations, faced two significant challenges. Firstly, they needed to maintain high-quality training data for their computer vision system. Secondly, they sought to automate their labeling process for faster throughput while retaining their in-house annotation team. Voxel had already invested in an in-house annotation team of subject matter experts, but they were struggling with efficiency in their labeling operations. They had been using an open-source solution, Computer Vision Annotation Tool (CVAT), which was causing bottlenecks as they increased the volume of annotations needed for model training. From an operational perspective, Voxel found it difficult to efficiently collect data and insights on the data labeling process, leading to significant manual effort. The tool couldn’t effectively link data quality to individual annotators, making it hard to identify the cause of low-quality labels. On the engineering side, Voxel had to custom-build data pipelines for new customer projects, a process that took multiple engineers four weeks for each project.

Voxel is a company that leverages AI and computer vision to change how companies manage risk and operations. They enhance their customers' security cameras with real-time AI to detect hazards, risky activities, and operational inefficiencies. Their technology delivers insights that can prevent injuries before they happen and improve the safety culture in hazardous and dynamic environments. To develop a robust computer vision system, Voxel requires large amounts of high-quality training data. They have an in-house annotation team of subject-matter experts who are well-versed in handling Voxel's specific use case.