用视频追踪与分割技术加速数据标注，提升训练集生成效率
Track Anything Annotate: Video annotation and dataset generation of computer vision models
- 基于视频追踪与分割构建自动化标注工具原型
- 相比人工标注，数据集生成速度显著提升
- 适合需要快速构建视觉模型训练数据的研究者
现代机器学习方法需要大量标注数据，导致准备过程耗时且资源消耗大。本文提出一种基于视频追踪与分割的标注与数据集生成工具原型。我们评估了从技术选型到最终实现的不同方案。所开发的原型相比人工标注大幅加快数据集生成速度。所有资源已公开于 https://github.com/lnikioffic/track-anything-annotate。
原文摘要 · Abstract (English)
Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available at https://github.com/lnikioffic/track-anything-annotate
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