Bud load management on table grape yield and quality – cv. Sugrathirteen (Midnight Beauty®)|葡萄种植数据集|农业研究数据集
收藏VoxBox
VoxBox是一个大规模语音语料库,由多样化的开源数据集构建而成,用于训练文本到语音(TTS)系统。
github 收录
CosyVoice 2
CosyVoice 2是由阿里巴巴集团开发的多语言语音合成数据集,旨在通过大规模多语言数据集训练,实现高质量的流式语音合成。数据集通过有限标量量化技术改进语音令牌的利用率,并结合预训练的大型语言模型作为骨干,支持流式和非流式合成。数据集的创建过程包括文本令牌化、监督语义语音令牌化、统一文本-语音语言模型和块感知流匹配模型等步骤。该数据集主要应用于语音合成领域,旨在解决高延迟和低自然度的问题,提供接近人类水平的语音合成质量。
arXiv 收录
全国 1∶200 000 数字地质图(公开版)空间数据库
As the only one of its kind, China National Digital Geological Map (Public Version at 1∶200 000 scale) Spatial Database (CNDGM-PVSD) is based on China' s former nationwide measured results of regional geological survey at 1∶200 000 scale, and is also one of the nationwide basic geosciences spatial databases jointly accomplished by multiple organizations of China. Spatially, it embraces 1 163 geological map-sheets (at scale 1: 200 000) in both formats of MapGIS and ArcGIS, covering 72% of China's whole territory with a total data volume of 90 GB. Its main sources is from 1∶200 000 regional geological survey reports, geological maps, and mineral resources maps with an original time span from mid-1950s to early 1990s. Approved by the State's related agencies, it meets all the related technical qualification requirements and standards issued by China Geological Survey in data integrity, logic consistency, location acc racy, attribution fineness, and collation precision, and is hence of excellent and reliable quality. The CNDGM-PVSD is an important component of China' s national spatial database categories, serving as a spatial digital platform for the information construction of the State's national economy, and providing informationbackbones to the national and provincial economic planning, geohazard monitoring, geological survey, mineral resources exploration as well as macro decision-making.
DataCite Commons 收录
AQA-7
AQA-7 是一个用于动作质量评估(AQA)的统一基准数据集,旨在通过整合多个领域的数据集来标准化评估方法。该数据集包含视频、骨骼数据和多模态输入,涵盖了体育分析、技能评估和医疗护理等多个应用领域。数据集的创建过程通过系统分析现有文献和实验协议,确保了评估的准确性和计算效率。AQA-7 的应用领域广泛,旨在解决动作质量评估中的偏差问题,提供客观的自动化评估,特别是在体育评分、技能评估和康复训练中具有重要意义。
arXiv 收录
DLLG数据集
DLLG数据集是一个包含道路垃圾图片的数据集,主要用于训练深度学习模型以识别和分类道路垃圾。数据集来源包括机器人视角拍摄、手机相机拍摄和网络图片,涵盖塑料袋、饮料瓶和易拉罐三类垃圾。数据集旨在增强训练网络的鲁棒性,通过不同时间、天气和光照条件下的图片收集,以及包含不同形态的垃圾案例。
github 收录