AI-exp-5/datasets/train/labels/-10-1_mp4-72_jpg.rf.cc65505d4485599451f3175729af0c60.txt

0 0.5594230805664062 0.9905473743489583 0.57189297265625 0.9887998561197917 0.5630670224609375 0.9137442923177083 0.5525155751953125 0.9152229609375001 0.5594230805664062 0.9905473743489583
1 0.48082504052734376 0.07958748177083333 0.48829718408203127 0.07595415950520833 0.4826513564453125 0.004332212239583333 0.47305913134765626 0.005676457031249999 0.48082504052734376 0.07958748177083333
2 0.49132435302734373 0.520353556640625 0.5173212822265625 0.5425722122395834 0.5420104702148437 0.52359519140625 0.550444732421875 0.4952582923177083 0.5394929077148437 0.46705195572916663 0.6586844775390625 0.05983480924479167 0.6492449282226562 0.05081292317708333 0.5149592924804688 0.45109369205729166 0.4888557514648437 0.47673437890625 0.49132435302734373 0.520353556640625
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 0.5594230805664062 0.9905473743489583 0.57189297265625 0.9887998561197917 0.5630670224609375 0.9137442923177083 0.5525155751953125 0.9152229609375001 0.5594230805664062 0.9905473743489583`
			`1 0.48082504052734376 0.07958748177083333 0.48829718408203127 0.07595415950520833 0.4826513564453125 0.004332212239583333 0.47305913134765626 0.005676457031249999 0.48082504052734376 0.07958748177083333`
			`2 0.49132435302734373 0.520353556640625 0.5173212822265625 0.5425722122395834 0.5420104702148437 0.52359519140625 0.550444732421875 0.4952582923177083 0.5394929077148437 0.46705195572916663 0.6586844775390625 0.05983480924479167 0.6492449282226562 0.05081292317708333 0.5149592924804688 0.45109369205729166 0.4888557514648437 0.47673437890625 0.49132435302734373 0.520353556640625`