BakuFlow: A Streamlining Semi-Automatic Label Generation Tool

TL;DR

BakuFlow is a semi-automatic labeling tool that combines manual corrections, data augmentation, label propagation, and an extended YOLOE framework to significantly reduce annotation time and improve efficiency in computer vision tasks.

Contribution

The paper introduces BakuFlow, a novel tool integrating live pixel-precise corrections, data augmentation, label propagation, and an extended YOLOE for flexible, scalable, and efficient data annotation.

Findings

Reduces manual labeling time in video datasets

Enhances annotation accuracy with live corrections

Supports dynamic object class addition and prompts

Abstract

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.