No-code MLOps Platform for Data Annotation
Hyeongbok Kim, Beomjin Kim, Wenxin Lu, Lu Li · 2023
The increasing demand for large-scale training data in deep learning models has underscored the significance of efficient data annotation. However, manual labeling remains a time-consuming and labor-intensive process. In this paper, we propose a No-code MLOps-based data annotation system that empowers non-developers to train and apply auto-labeling models seamlessly. The system supports various annotation types, including bounding boxes, keypoints, and 3D point clouds, enabling versatile applications. We present experiments on object detection, custom keypoint estimation, and 3D point cloud segmentation, demonstrating the system's effectiveness. Our contributions include a user-friendly platform, flexible annotation support, and interactive management features, marking a step towards democratizing data annotation.