ComView/datasets/train/labels/trial00001_0008_jpg.rf.509f06e2dd1d19dbbdef8066869b2648.txt

1 0.3629807692307692 0.15144230769230768 0.0829326923076923 0.14903846153846154
1 0.49759615384615385 0.13942307692307693 0.07932692307692307 0.14302884615384615
1 0.625 0.14423076923076922 0.0889423076923077 0.15504807692307693
1 0.5600961538461539 0.4963942307692308 0.08413461538461539 0.1502403846153846
1 0.171875 0.7355769230769231 0.09375 0.14423076923076922
0 0.17307692307692307 0.5048076923076923 0.09375 0.12740384615384615
0 0.296875 0.5024038461538461 0.0889423076923077 0.140625
0 0.4266826923076923 0.5036057692307693 0.08653846153846154 0.14182692307692307
0 0.6959134615384616 0.5 0.08653846153846154 0.13942307692307693
0 0.8233173076923077 0.4963942307692308 0.08653846153846154 0.14903846153846154
0 0.828125 0.7367788461538461 0.09254807692307693 0.15264423076923078
0 0.6971153846153846 0.7391826923076923 0.08533653846153846 0.14543269230769232
0 0.5625 0.7379807692307693 0.08774038461538461 0.14783653846153846
0 0.42908653846153844 0.7403846153846154 0.07932692307692307 0.14423076923076922
0 0.29927884615384615 0.7367788461538461 0.08653846153846154 0.13942307692307693
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 0.3629807692307692 0.15144230769230768 0.0829326923076923 0.14903846153846154`
			`1 0.49759615384615385 0.13942307692307693 0.07932692307692307 0.14302884615384615`
			`1 0.625 0.14423076923076922 0.0889423076923077 0.15504807692307693`
			`1 0.5600961538461539 0.4963942307692308 0.08413461538461539 0.1502403846153846`
			`1 0.171875 0.7355769230769231 0.09375 0.14423076923076922`
			`0 0.17307692307692307 0.5048076923076923 0.09375 0.12740384615384615`
			`0 0.296875 0.5024038461538461 0.0889423076923077 0.140625`
			`0 0.4266826923076923 0.5036057692307693 0.08653846153846154 0.14182692307692307`
			`0 0.6959134615384616 0.5 0.08653846153846154 0.13942307692307693`
			`0 0.8233173076923077 0.4963942307692308 0.08653846153846154 0.14903846153846154`
			`0 0.828125 0.7367788461538461 0.09254807692307693 0.15264423076923078`
			`0 0.6971153846153846 0.7391826923076923 0.08533653846153846 0.14543269230769232`
			`0 0.5625 0.7379807692307693 0.08774038461538461 0.14783653846153846`
			`0 0.42908653846153844 0.7403846153846154 0.07932692307692307 0.14423076923076922`
			`0 0.29927884615384615 0.7367788461538461 0.08653846153846154 0.13942307692307693`