ComView/datasets/train/labels/img13_jpg.rf.36ccf4f7cda6d798914c73912fe36fe9.txt

1 0.09975961538461539 0.43990384615384615 0.06971153846153846 0.1201923076923077
1 0.3762019230769231 0.4182692307692308 0.06490384615384616 0.11057692307692307
1 0.6490384615384616 0.4014423076923077 0.06971153846153846 0.13221153846153846
0 0.23798076923076922 0.42788461538461536 0.0673076923076923 0.10817307692307693
0 0.07091346153846154 0.7536057692307693 0.06971153846153846 0.10697115384615384
0 0.21995192307692307 0.7487980769230769 0.07572115384615384 0.12379807692307693
0 0.3701923076923077 0.7331730769230769 0.0733173076923077 0.1201923076923077
0 0.5180288461538461 0.7235576923076923 0.07451923076923077 0.12379807692307693
0 0.5132211538461539 0.41225961538461536 0.06610576923076923 0.11298076923076923
0 0.7788461538461539 0.39903846153846156 0.0733173076923077 0.11538461538461539
0 0.9134615384615384 0.390625 0.07572115384615384 0.1141826923076923
0 0.8125 0.6983173076923077 0.07211538461538461 0.12139423076923077
0 0.9579326923076923 0.6947115384615384 0.0733173076923077 0.1201923076923077
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 0.09975961538461539 0.43990384615384615 0.06971153846153846 0.1201923076923077`
			`1 0.3762019230769231 0.4182692307692308 0.06490384615384616 0.11057692307692307`
			`1 0.6490384615384616 0.4014423076923077 0.06971153846153846 0.13221153846153846`
			`0 0.23798076923076922 0.42788461538461536 0.0673076923076923 0.10817307692307693`
			`0 0.07091346153846154 0.7536057692307693 0.06971153846153846 0.10697115384615384`
			`0 0.21995192307692307 0.7487980769230769 0.07572115384615384 0.12379807692307693`
			`0 0.3701923076923077 0.7331730769230769 0.0733173076923077 0.1201923076923077`
			`0 0.5180288461538461 0.7235576923076923 0.07451923076923077 0.12379807692307693`
			`0 0.5132211538461539 0.41225961538461536 0.06610576923076923 0.11298076923076923`
			`0 0.7788461538461539 0.39903846153846156 0.0733173076923077 0.11538461538461539`
			`0 0.9134615384615384 0.390625 0.07572115384615384 0.1141826923076923`
			`0 0.8125 0.6983173076923077 0.07211538461538461 0.12139423076923077`
			`0 0.9579326923076923 0.6947115384615384 0.0733173076923077 0.1201923076923077`