ComView/datasets/train/labels/trial01_0007_jpg.rf.fe40c533022a919385f3161d6a5a02fd.txt

1 0.19471153846153846 0.5288461538461539 0.054086538461538464 0.08533653846153846
1 0.25841346153846156 0.5336538461538461 0.051682692307692304 0.09254807692307693
1 0.18269230769230768 0.6574519230769231 0.056490384615384616 0.09615384615384616
1 0.24519230769230768 0.6622596153846154 0.057692307692307696 0.09615384615384616
1 0.25600961538461536 0.026442307692307692 0.040865384615384616 0.051682692307692304
1 0.3088942307692308 0.027644230769230768 0.038461538461538464 0.055288461538461536
1 0.359375 0.03125 0.04567307692307692 0.0625
1 0.7728365384615384 0.1935096153846154 0.05048076923076923 0.08533653846153846
0 0.18870192307692307 0.59375 0.018028846153846152 0.03125
0 0.21033653846153846 0.5997596153846154 0.014423076923076924 0.028846153846153848
0 0.23197115384615385 0.6009615384615384 0.015625 0.028846153846153848
0 0.25240384615384615 0.6021634615384616 0.016826923076923076 0.027644230769230768
0 0.6790865384615384 0.06009615384615385 0.042067307692307696 0.07451923076923077
0 0.7259615384615384 0.07932692307692307 0.04326923076923077 0.08173076923076923
0 0.7608173076923077 0.12379807692307693 0.042067307692307696 0.07692307692307693
feat(datasets): 添加新的训练数据标签 - 在 datasets/train/labels 目录下新增了多个标签文件 - 文件命名格式为：视频ID-帧序号.jpg.rf.哈希值.txt -标签内容包括物体类别和位置信息，用于训练目标检测模型 2025-05-29 11:59:57 +00:00			`1 0.19471153846153846 0.5288461538461539 0.054086538461538464 0.08533653846153846`
			`1 0.25841346153846156 0.5336538461538461 0.051682692307692304 0.09254807692307693`
			`1 0.18269230769230768 0.6574519230769231 0.056490384615384616 0.09615384615384616`
			`1 0.24519230769230768 0.6622596153846154 0.057692307692307696 0.09615384615384616`
			`1 0.25600961538461536 0.026442307692307692 0.040865384615384616 0.051682692307692304`
			`1 0.3088942307692308 0.027644230769230768 0.038461538461538464 0.055288461538461536`
			`1 0.359375 0.03125 0.04567307692307692 0.0625`
			`1 0.7728365384615384 0.1935096153846154 0.05048076923076923 0.08533653846153846`
			`0 0.18870192307692307 0.59375 0.018028846153846152 0.03125`
			`0 0.21033653846153846 0.5997596153846154 0.014423076923076924 0.028846153846153848`
			`0 0.23197115384615385 0.6009615384615384 0.015625 0.028846153846153848`
			`0 0.25240384615384615 0.6021634615384616 0.016826923076923076 0.027644230769230768`
			`0 0.6790865384615384 0.06009615384615385 0.042067307692307696 0.07451923076923077`
			`0 0.7259615384615384 0.07932692307692307 0.04326923076923077 0.08173076923076923`
			`0 0.7608173076923077 0.12379807692307693 0.042067307692307696 0.07692307692307693`