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epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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1, 0.89919, 1.0126, 1.3448, 0.90511, 0.81949, 0.91309, 0.70686, 0.77371, 0.67242, 1.2763, 0.00023739, 0.00023739, 0.00023739
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2, 0.91216, 1.0081, 1.3561, 0.89014, 0.792, 0.89189, 0.65996, 0.87412, 0.76363, 1.3803, 0.00043617, 0.00043617, 0.00043617
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3, 0.91206, 0.86003, 1.4574, 0.83504, 0.77908, 0.86936, 0.63414, 0.92741, 0.82188, 1.4285, 0.00059568, 0.00059568, 0.00059568
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4, 0.93238, 0.86111, 1.4709, 0.86782, 0.78551, 0.87481, 0.65073, 0.88728, 0.75628, 1.3806, 0.00053728, 0.00053728, 0.00053728
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5, 0.91087, 0.82626, 1.4488, 0.8688, 0.81398, 0.89649, 0.67192, 0.86705, 0.72792, 1.3618, 0.00047838, 0.00047838, 0.00047838
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6, 0.8873, 0.79748, 1.4309, 0.86827, 0.82929, 0.9009, 0.67679, 0.85067, 0.72071, 1.3402, 0.00041947, 0.00041947, 0.00041947
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7, 0.85296, 0.76364, 1.4028, 0.89277, 0.80801, 0.9097, 0.70847, 0.8024, 0.66936, 1.2992, 0.00036057, 0.00036057, 0.00036057
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8, 0.82769, 0.7302, 1.3715, 0.89685, 0.81286, 0.9122, 0.70948, 0.78609, 0.65393, 1.2749, 0.00030167, 0.00030167, 0.00030167
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9, 0.79556, 0.68941, 1.3427, 0.92671, 0.83319, 0.92502, 0.72868, 0.74694, 0.62542, 1.2375, 0.00024276, 0.00024276, 0.00024276
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10, 0.78018, 0.66899, 1.3359, 0.9157, 0.82795, 0.9279, 0.74484, 0.71897, 0.6113, 1.2166, 0.00018385, 0.00018385, 0.00018385
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11, 0.75546, 0.64417, 1.3085, 0.93088, 0.84309, 0.93684, 0.75865, 0.70497, 0.58641, 1.1976, 0.00012495, 0.00012495, 0.00012495
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12, 0.74403, 0.62852, 1.3007, 0.90644, 0.86694, 0.93731, 0.75826, 0.68976, 0.58106, 1.1856, 6.6045e-05, 6.6045e-05, 6.6045e-05
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