AI-exp-5/datasets/train/labels/-10-1_mp4-11_jpg.rf.693379eddd9d78b04b559a08e68a6aab.txt

0 0.47453352294921874 0.99181173046875 0.48985059375 0.9943193300781249 0.4924862314453125 0.92348919140625 0.48004111181640624 0.9214517669270833 0.47453352294921874 0.99181173046875
2 0.49029644921875 0.535726583984375 0.36976548291015626 0.9370855546875 0.3788684765625 0.9437584941406251 0.5181931494140625 0.5493632903645833 0.5477015283203125 0.5243938723958333 0.5510933295898437 0.47700951432291666 0.5169655859375 0.4571700423177083 0.48315776416015627 0.4866182506510417 0.49029644921875 0.535726583984375
1 0.554276625 0.009795259765625 0.54853394921875 0.08270806901041666 0.56121415625 0.08219264778645834 0.5686363784179688 0.012146134765624999 0.554276625 0.009795259765625
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 0.47453352294921874 0.99181173046875 0.48985059375 0.9943193300781249 0.4924862314453125 0.92348919140625 0.48004111181640624 0.9214517669270833 0.47453352294921874 0.99181173046875`
			`2 0.49029644921875 0.535726583984375 0.36976548291015626 0.9370855546875 0.3788684765625 0.9437584941406251 0.5181931494140625 0.5493632903645833 0.5477015283203125 0.5243938723958333 0.5510933295898437 0.47700951432291666 0.5169655859375 0.4571700423177083 0.48315776416015627 0.4866182506510417 0.49029644921875 0.535726583984375`
			`1 0.554276625 0.009795259765625 0.54853394921875 0.08270806901041666 0.56121415625 0.08219264778645834 0.5686363784179688 0.012146134765624999 0.554276625 0.009795259765625`