SCB-Dataset：一个用于检测学生与教师课堂行为的数据集
SCB-Dataset: A Dataset for Detecting Student and Teacher Classroom Behavior
AI总结：
本文构建了涵盖19个类别、包含目标检测与图像分类任务的SCB-Dataset师生课堂行为数据集，并使用YOLO系列算法和大型视觉语言模型进行基准测试，为人工智能在教育领域的应用奠定了基础。
摘要：
使用深度学习方法检测学生和教师的课堂行为，是自动分析课堂表现和提升教学效果的有效途径。然而，目前仍然缺乏公开的高质量师生行为数据集。我们构建了SCB-Dataset，这是一个涵盖19个类别的全面的学生与教师课堂行为数据集。SCB-Dataset分为两种类型：目标检测和图像分类。目标检测部分包含13,330张图像和122,977个标签，图像分类部分包含21,019张图像。我们使用YOLO系列算法和大型视觉语言模型在SCB-Dataset上进行了基准测试。我们相信，SCB-Dataset能够为未来人工智能在教育领域的应用提供坚实的基础。代码：https://github.com/Whiffe/SCB-dataset
英文摘要：
Using deep learning methods to detect the classroom behaviors of both students and teachers is an effective way to automatically analyze classroom performance and enhance teaching effectiveness. Then, there is still a scarcity of publicly available high-quality datasets on student-teacher behaviors. We constructed SCB-Dataset a comprehensive dataset of student and teacher classroom behaviors covering 19 classes. SCB-Dataset is divided into two types: Object Detection and Image Classification. The Object Detection part includes 13,330 images and 122,977 labels, and the Image Classification part includes 21,019 images. We conducted benchmark tests on SCB-Dataset using YOLO series algorithms and Large vision-language model. We believe that SCB-Dataset can provide a solid foundation for future applications of artificial intelligence in education. Code:https://github.com/Whiffe/SCB-dataset
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
