ComView/datasets/train/labels/trial01_0007_jpg.rf.cf86b1bcae24c509cb4a1270ff646f91.txt

0 0.46274038461538464 0.5120192307692307 0.042067307692307696 0.08533653846153846
0 0.45913461538461536 0.6346153846153846 0.052884615384615384 0.08173076923076923
0 0.515625 0.5048076923076923 0.05048076923076923 0.09375
0 0.5144230769230769 0.6490384615384616 0.04567307692307692 0.09615384615384616
0 0.578125 0.6634615384615384 0.055288461538461536 0.1045673076923077
0 0.6538461538461539 0.5084134615384616 0.06610576923076923 0.09975961538461539
0 0.7475961538461539 0.5132211538461539 0.06971153846153846 0.11057692307692307
0 0.8533653846153846 0.5084134615384616 0.07692307692307693 0.11899038461538461
0 0.65625 0.6790865384615384 0.06009615384615385 0.10817307692307693
0 0.7463942307692307 0.6983173076923077 0.07451923076923077 0.1201923076923077
1 0.5456730769230769 0.28365384615384615 0.04567307692307692 0.09254807692307693
1 0.6165865384615384 0.2620192307692308 0.05889423076923077 0.09735576923076923
1 0.6995192307692307 0.2439903846153846 0.06490384615384616 0.11298076923076923
1 0.5793269230769231 0.5084134615384616 0.052884615384615384 0.10216346153846154
1 0.8617788461538461 0.7259615384615384 0.0889423076923077 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			`0 0.46274038461538464 0.5120192307692307 0.042067307692307696 0.08533653846153846`
			`0 0.45913461538461536 0.6346153846153846 0.052884615384615384 0.08173076923076923`
			`0 0.515625 0.5048076923076923 0.05048076923076923 0.09375`
			`0 0.5144230769230769 0.6490384615384616 0.04567307692307692 0.09615384615384616`
			`0 0.578125 0.6634615384615384 0.055288461538461536 0.1045673076923077`
			`0 0.6538461538461539 0.5084134615384616 0.06610576923076923 0.09975961538461539`
			`0 0.7475961538461539 0.5132211538461539 0.06971153846153846 0.11057692307692307`
			`0 0.8533653846153846 0.5084134615384616 0.07692307692307693 0.11899038461538461`
			`0 0.65625 0.6790865384615384 0.06009615384615385 0.10817307692307693`
			`0 0.7463942307692307 0.6983173076923077 0.07451923076923077 0.1201923076923077`
			`1 0.5456730769230769 0.28365384615384615 0.04567307692307692 0.09254807692307693`
			`1 0.6165865384615384 0.2620192307692308 0.05889423076923077 0.09735576923076923`
			`1 0.6995192307692307 0.2439903846153846 0.06490384615384616 0.11298076923076923`
			`1 0.5793269230769231 0.5084134615384616 0.052884615384615384 0.10216346153846154`
			`1 0.8617788461538461 0.7259615384615384 0.0889423076923077 0.13942307692307693`