AI-exp-5/datasets/train/labels/-10-1_mp4-6_jpg.rf.36ba462510dcb9f76be4084360d1eba6.txt

2 0.4847011196289063 0.48251275390625 0.48518125146484375 0.5241594153645833 0.4950318881835937 0.5415890625 0.41363494677734375 0.9626815182291667 0.4270066220703125 0.9651872063802083 0.5245063642578125 0.5484108658854167 0.5490090629882812 0.5192375611979166 0.54661869140625 0.4772329446614583 0.5119349438476563 0.4551498756510417 0.4847011196289063 0.48251275390625
0 0.47249098046875 0.9217645572916666 0.46619750146484373 0.990712150390625 0.47970340966796876 0.9919443463541667 0.4870862421875 0.9219022369791667 0.47249098046875 0.9217645572916666
1 0.5597374907226562 0.0108798359375 0.5541306640625 0.08255339322916667 0.5665472197265625 0.08488010351562501 0.57406428515625 0.013564501953125 0.5597374907226562 0.0108798359375
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 0.4847011196289063 0.48251275390625 0.48518125146484375 0.5241594153645833 0.4950318881835937 0.5415890625 0.41363494677734375 0.9626815182291667 0.4270066220703125 0.9651872063802083 0.5245063642578125 0.5484108658854167 0.5490090629882812 0.5192375611979166 0.54661869140625 0.4772329446614583 0.5119349438476563 0.4551498756510417 0.4847011196289063 0.48251275390625`
			`0 0.47249098046875 0.9217645572916666 0.46619750146484373 0.990712150390625 0.47970340966796876 0.9919443463541667 0.4870862421875 0.9219022369791667 0.47249098046875 0.9217645572916666`
			`1 0.5597374907226562 0.0108798359375 0.5541306640625 0.08255339322916667 0.5665472197265625 0.08488010351562501 0.57406428515625 0.013564501953125 0.5597374907226562 0.0108798359375`