ComView/datasets/train/labels/trial001_0001_jpg.rf.494f3f55dccb368e2a2b9def21825f99.txt

1 0.5456730769230769 0.2860576923076923 0.052884615384615384 0.09735576923076923
1 0.6153846153846154 0.265625 0.055288461538461536 0.09254807692307693
1 0.6995192307692307 0.23798076923076922 0.06129807692307692 0.09975961538461539
1 0.578125 0.5108173076923077 0.054086538461538464 0.09495192307692307
1 0.6538461538461539 0.5132211538461539 0.05889423076923077 0.10336538461538461
0 0.46153846153846156 0.5108173076923077 0.046875 0.08653846153846154
0 0.515625 0.5084134615384616 0.04567307692307692 0.08173076923076923
0 0.45913461538461536 0.6394230769230769 0.046875 0.08653846153846154
0 0.515625 0.6490384615384616 0.04807692307692308 0.09375
0 0.5793269230769231 0.6622596153846154 0.057692307692307696 0.09735576923076923
0 0.6526442307692307 0.6826923076923077 0.056490384615384616 0.109375
0 0.7475961538461539 0.5168269230769231 0.0673076923076923 0.11057692307692307
0 0.7463942307692307 0.6983173076923077 0.07451923076923077 0.11538461538461539
0 0.8617788461538461 0.7199519230769231 0.0829326923076923 0.12740384615384615
0 0.859375 0.515625 0.07451923076923077 0.13221153846153846
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 0.5456730769230769 0.2860576923076923 0.052884615384615384 0.09735576923076923`
			`1 0.6153846153846154 0.265625 0.055288461538461536 0.09254807692307693`
			`1 0.6995192307692307 0.23798076923076922 0.06129807692307692 0.09975961538461539`
			`1 0.578125 0.5108173076923077 0.054086538461538464 0.09495192307692307`
			`1 0.6538461538461539 0.5132211538461539 0.05889423076923077 0.10336538461538461`
			`0 0.46153846153846156 0.5108173076923077 0.046875 0.08653846153846154`
			`0 0.515625 0.5084134615384616 0.04567307692307692 0.08173076923076923`
			`0 0.45913461538461536 0.6394230769230769 0.046875 0.08653846153846154`
			`0 0.515625 0.6490384615384616 0.04807692307692308 0.09375`
			`0 0.5793269230769231 0.6622596153846154 0.057692307692307696 0.09735576923076923`
			`0 0.6526442307692307 0.6826923076923077 0.056490384615384616 0.109375`
			`0 0.7475961538461539 0.5168269230769231 0.0673076923076923 0.11057692307692307`
			`0 0.7463942307692307 0.6983173076923077 0.07451923076923077 0.11538461538461539`
			`0 0.8617788461538461 0.7199519230769231 0.0829326923076923 0.12740384615384615`
			`0 0.859375 0.515625 0.07451923076923077 0.13221153846153846`