BakuFlow: A Streamlining Semi-Automatic Label Generation Tool
This addresses the problem of time-consuming and error-prone labeling for practitioners in computer vision and industrial applications, though it is incremental as it builds on existing tools and methods.
The paper tackles the bottleneck of manual data labeling in computer vision by introducing BakuFlow, a semi-automatic tool that reduces labeling workload through features like label propagation for video data and an automatic module based on a modified YOLOE framework.
Accurately labeling (or annotation) data is still a bottleneck in computer vision, especially for large-scale tasks where manual labeling is time-consuming and error-prone. While tools like LabelImg can handle the labeling task, some of them still require annotators to manually label each image. In this paper, we introduce BakuFlow, a streamlining semi-automatic label generation tool. Key features include (1) a live adjustable magnifier for pixel-precise manual corrections, improving user experience; (2) an interactive data augmentation module to diversify training datasets; (3) label propagation for rapidly copying labeled objects between consecutive frames, greatly accelerating annotation of video data; and (4) an automatic labeling module powered by a modified YOLOE framework. Unlike the original YOLOE, our extension supports adding new object classes and any number of visual prompts per class during annotation, enabling flexible and scalable labeling for dynamic, real-world datasets. These innovations make BakuFlow especially effective for object detection and tracking, substantially reducing labeling workload and improving efficiency in practical computer vision and industrial scenarios.