CherryChèvre:用于在自然环境中检测山羊的细粒度数据集
Jehan-Antoine Vayssade1, Rémy Arquet2, Willy Troupe2
1INRAe - ASSET, Animal Genetic, 97170 Petit-Bourg, Guadeloupe. javayss@sleek-think.ovh.
Scientific data
|October 11, 2023
概括
一个新的数据集包含6160个注释图像,帮助机器学习进行山羊检测. 该资源支持精准农业,动物福利和计算机视觉研究的进步.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业科学 农业科学
背景情况:
- 精确的山羊检测对于精准农业,动物福利和畜牧业至关重要.
- 现有的数据集可能缺乏环境条件的多样性,限制了算法概括.
研究的目的:
- 介绍一种新的,大规模的数据集用于山羊检测.
- 为在农业环境中评估机器学习算法提供一个基准.
主要方法:
- 收集了6160张不同环境条件下的山羊图像.
- 专家注释者确保了图像标签的高准确性和一致性.
- 数据集是公开提供用于研究和开发.
主要成果:
- 数据集包括6160张精心注释的图像.
- 图像捕捉山羊在各种设置,对于强大的模型训练至关重要.
- 该数据集作为山羊检测算法的标准化基准.
结论:
- 这一数据集在农业领域显著推进了计算机视觉研究.
- 促进了改进的山羊监测和管理系统的开发.
- 能够进一步研究动物行为分析和动物福利.
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