BakuFlow 用自动标注和智能工具，让图像视频标注快得多。
BakuFlow: A Streamlining Semi-Automatic Label Generation Tool
- 结合自动标注与交互修正，实现半自动高效标注。
- 视频标注中对象可跨帧快速传播，提速显著。
- 支持新增类别和多提示，适合动态真实场景。
准确的数据标注仍是计算机视觉中的瓶颈，尤其在大规模任务中，人工标注耗时且易出错。现有工具如LabelImg仍需逐图手动标注。本文提出BakuFlow，一款流式半自动标注工具，包含：（1）实时可调放大镜，实现像素级修正，提升体验；（2）交互式数据增强模块，丰富训练数据；（3）标签传播功能，可在连续帧间快速复制已标注对象，大幅加速视频标注；（4）基于改进YOLOE框架的自动标注模块，支持在标注过程中新增物体类别及每类多个视觉提示，实现灵活可扩展的标注。该工具显著降低标注工作量，提升实际应用中目标检测与跟踪任务的效率。
原文摘要 · Abstract (English)
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.
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