AI-exp-5/datasets/train/labels/-10-1_mp4-25_jpg.rf.d9e6204157d7ff6a5bc7b40d984615db.txt

0 0.860426572265625 0.36840181510416664 0.8563338598632813 0.35509960286458336 0.8010725903320313 0.37339060807291663 0.8030779111328125 0.38596966601562505 0.860426572265625 0.36840181510416664
1 0.13564942333984376 0.5909016002604167 0.1852501171875 0.5742148522135416 0.18210083447265624 0.560645263671875 0.133042505859375 0.5745488235677083 0.13564942333984376 0.5909016002604167
2 0.4698104086914062 0.45412582356770836 0.46484705224609374 0.49103041992187496 0.4849552255859375 0.5182000481770833 0.5131168061523438 0.5154777630208334 0.76937773828125 0.7807389466145834 0.7766654096679687 0.7707851263020834 0.5295197778320313 0.4884784641927083 0.52296144921875 0.4535244765625 0.4989382578125 0.43272335221354163 0.4698104086914062 0.45412582356770836
feat(datasets): 添加新的训练和验证数据标签 - 在 train 和 valid 文件夹中添加了多个新的标签文件 - 标签文件包含了不同类型的目标检测框坐标和类别 - 新增数据有助于模型训练和性能提升 2025-06-06 10:07:28 +00:00			`0 0.860426572265625 0.36840181510416664 0.8563338598632813 0.35509960286458336 0.8010725903320313 0.37339060807291663 0.8030779111328125 0.38596966601562505 0.860426572265625 0.36840181510416664`
			`1 0.13564942333984376 0.5909016002604167 0.1852501171875 0.5742148522135416 0.18210083447265624 0.560645263671875 0.133042505859375 0.5745488235677083 0.13564942333984376 0.5909016002604167`
			`2 0.4698104086914062 0.45412582356770836 0.46484705224609374 0.49103041992187496 0.4849552255859375 0.5182000481770833 0.5131168061523438 0.5154777630208334 0.76937773828125 0.7807389466145834 0.7766654096679687 0.7707851263020834 0.5295197778320313 0.4884784641927083 0.52296144921875 0.4535244765625 0.4989382578125 0.43272335221354163 0.4698104086914062 0.45412582356770836`