AI-exp-5/datasets/train/labels/-10-1_mp4-93_jpg.rf.6153779175dddab0581e6bbf882cb092.txt

1 0.4575920561523438 0.08008539388020834 0.46709058740234377 0.07785226367187499 0.455979220703125 0.008642991536458334 0.44743054248046876 0.01065280859375 0.4575920561523438 0.08008539388020834
0 0.5839926166992188 0.98404887890625 0.5944410014648438 0.9815924361979166 0.5861791938476563 0.9117132252604166 0.5738311030273437 0.9146162936197917 0.5839926166992188 0.98404887890625
2 0.49627175537109375 0.46143994140625 0.48526122314453124 0.496674720703125 0.4977171059570312 0.531615873046875 0.5256528720703125 0.5354948763020834 0.5456571630859375 0.5164274980468749 0.8716677412109375 0.5599194720052083 0.872842453125 0.5465848235677083 0.5492363544921876 0.48424569270833334 0.5258846879882813 0.4557837096354167 0.49627175537109375 0.46143994140625
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			`1 0.4575920561523438 0.08008539388020834 0.46709058740234377 0.07785226367187499 0.455979220703125 0.008642991536458334 0.44743054248046876 0.01065280859375 0.4575920561523438 0.08008539388020834`
			`0 0.5839926166992188 0.98404887890625 0.5944410014648438 0.9815924361979166 0.5861791938476563 0.9117132252604166 0.5738311030273437 0.9146162936197917 0.5839926166992188 0.98404887890625`
			`2 0.49627175537109375 0.46143994140625 0.48526122314453124 0.496674720703125 0.4977171059570312 0.531615873046875 0.5256528720703125 0.5354948763020834 0.5456571630859375 0.5164274980468749 0.8716677412109375 0.5599194720052083 0.872842453125 0.5465848235677083 0.5492363544921876 0.48424569270833334 0.5258846879882813 0.4557837096354167 0.49627175537109375 0.46143994140625`