AI-exp-5/datasets/train/labels/-10-1_mp4-110_jpg.rf.8a8028a92aeaec5f45603fd7a69d4646.txt

2 0.548844052734375 0.5077627486979167 0.5420507177734375 0.46947529036458335 0.5214044970703124 0.4549950944010417 0.4984050932617187 0.46121313867187497 0.48375659326171877 0.49149140690104165 0.48945136669921874 0.5246828463541667 0.5111288271484375 0.5416886113281251 0.5774495322265625 0.9735363841145833 0.5908770712890625 0.9703376002604167 0.5360897294921875 0.5366660305989583 0.548844052734375 0.5077627486979167
0 0.5306887709960938 0.9937287024739584 0.543219712890625 0.9931452493489584 0.5413683696289062 0.9224578841145834 0.5297676860351562 0.9217112311197916 0.5306887709960938 0.9937287024739584
1 0.4980225400390625 0.0044197519531249995 0.5008714619140625 0.07634746158854167 0.51144090625 0.074568544921875 0.5106208037109375 0.006406748046875 0.4980225400390625 0.0044197519531249995
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			`2 0.548844052734375 0.5077627486979167 0.5420507177734375 0.46947529036458335 0.5214044970703124 0.4549950944010417 0.4984050932617187 0.46121313867187497 0.48375659326171877 0.49149140690104165 0.48945136669921874 0.5246828463541667 0.5111288271484375 0.5416886113281251 0.5774495322265625 0.9735363841145833 0.5908770712890625 0.9703376002604167 0.5360897294921875 0.5366660305989583 0.548844052734375 0.5077627486979167`
			`0 0.5306887709960938 0.9937287024739584 0.543219712890625 0.9931452493489584 0.5413683696289062 0.9224578841145834 0.5297676860351562 0.9217112311197916 0.5306887709960938 0.9937287024739584`
			`1 0.4980225400390625 0.0044197519531249995 0.5008714619140625 0.07634746158854167 0.51144090625 0.074568544921875 0.5106208037109375 0.006406748046875 0.4980225400390625 0.0044197519531249995`