AI-exp-5/datasets/train/labels/-10-1_mp4-78_jpg.rf.7713061452479e672c1e298f04160cc0.txt

0 0.5784457104492188 0.9844583020833334 0.5889246567382812 0.9822453658854168 0.5785700014648437 0.9115178990885416 0.5709489497070313 0.9131273072916667 0.5784457104492188 0.9844583020833334
1 0.46354755126953123 0.082973046875 0.4741773798828125 0.08203028645833334 0.46352096044921876 0.00876246875 0.4539946455078125 0.010774228515625 0.46354755126953123 0.082973046875
2 0.5077559794921875 0.45513442057291664 0.4886086708984375 0.474802396484375 0.48571230029296875 0.5079650123697916 0.501066810546875 0.538575458984375 0.5328054140625 0.5344770013020833 0.5503965634765625 0.5099294993489584 0.5437666186523438 0.47877864388020835 0.7489760131835937 0.134671787109375 0.7420531684570313 0.12571744075520833 0.5277612407226563 0.45090972395833334 0.5077559794921875 0.45513442057291664
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 0.5784457104492188 0.9844583020833334 0.5889246567382812 0.9822453658854168 0.5785700014648437 0.9115178990885416 0.5709489497070313 0.9131273072916667 0.5784457104492188 0.9844583020833334`
			`1 0.46354755126953123 0.082973046875 0.4741773798828125 0.08203028645833334 0.46352096044921876 0.00876246875 0.4539946455078125 0.010774228515625 0.46354755126953123 0.082973046875`
			`2 0.5077559794921875 0.45513442057291664 0.4886086708984375 0.474802396484375 0.48571230029296875 0.5079650123697916 0.501066810546875 0.538575458984375 0.5328054140625 0.5344770013020833 0.5503965634765625 0.5099294993489584 0.5437666186523438 0.47877864388020835 0.7489760131835937 0.134671787109375 0.7420531684570313 0.12571744075520833 0.5277612407226563 0.45090972395833334 0.5077559794921875 0.45513442057291664`