【日志
repvgg-v5s
- bs512
- bs128
- v5s-lite-g
- v5s-g
- v5m-bs128
- yolov5s
- repvgg-v5s1
- repvgg-v5s2-not-fuse
- repvgg-v5s2-fuse
- bs128-v5m-only3-repvgg-失败
- v5m
v5lite-g-sppf-conv6-chunk 36.7map
v5lite-g-sppf-conv6 0.3645
v5-g2 0.3559
yolov5s-LC3-chunk2-rep-512 0.36192
LCB4 0.35085,
chunk2 0.35664
shuffle 0.35926
bs512
| map50-95 | v5s(0.357) | v5s-C3-shuffle | v5s-LC3chunk2 | v5s-LCB5( 0.3661) |
|---|---|---|---|---|
| epoch10 | 0.14827 | 0.14992 | 0.15495 | |
| epoch20 | 0.21336 | 0.21745 | 0.22601 | |
| epoch30 | 0.25346 | 0.25368 | 0.26217 | |
| epoch50 | 0.28785 | 0.288 | 0.29716 | |
| epoch80 | 0.30128 | ---- | 0.31201 | 0.3137 |
| epoch150 | 0.30411 | ---- | 0.31427 | 0.314 |
| map50-95 | v5s | v5s-LCBlock3 | v5s-LCBlock4 |
|---|---|---|---|
| epoch10 | 0.14827 | 0.15102 | 0.14323 |
| epoch20 | 0.21336 | 0.22605 | 0.21101 |
| epoch30 | 0.25346 | 0.26368 | 0.25108 |
| epoch50 | 0.28785 | 0.29997 | 0.28791 |
| epoch80 | 0.30128 | 0.31368 | 0.30026 |
| epoch150 | 0.30411 | 0.31626 | 0.29736 |
| epoch200 | xxxxxxxxxxx | 0.32948 | 0.31126 |
map50-95 | v5s | v5s-LCB3-chunk2|chunk2
-------- | ----- |
epoch10 | 0.14827 | 0.15495 |
epoch20 | 0.21336 | 0.22601
epoch30 | 0.25346 | 0.26217
epoch50 | 0.28785 | 0.29716
epoch80 | 0.30128 | 0.31201
epoch150 | 0.30411 | 0.31427 | 0.301
epoch200|xxxxxxxxxxx |
bs128
| map50-95 | v5s | v5s-g | v5s-lite-g |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21338 | 0.21598 |
| epoch20 | 0.24671 | 0.25771 | 0.25994 |
| epoch30 | 0.2564 | 0.26827 | 0.27078 |
| epoch50 | 0.27553 | 0.28786 | 0.29147 |
| epoch80 | 0.30649 | 0.31702 | 0.32149 |
| epoch150 | 0.34457 0.34509(sppf) |
| map50-95 | v5s | v5s-g-sppf | v5s-repconv |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21671 | 0.20472 |
| epoch20 | 0.24671 | 0.26189 | 0.24597 |
| epoch30 | 0.2564 | 0.27277 | 0.25588 |
| epoch50 | 0.27553 | 0.2919 | 0.27353 |
| epoch80 | 0.30649 | 0.31806 | 0.30048 |
| map50-95 | v5s | v5g-Focus->6 | v5s-lite-g |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21466 | 0.17235 |
| epoch20 | 0.24671 | 0.25808 | 0.21379 |
| epoch30 | 0.2564 | 0.26928 | 0.21894 |
| epoch50 | 0.27553 | 0.28971 | 0.23417 |
| epoch80 | 0.30649 | 0.31824 | ---- |
| map50-95 | v5s | v5-LCBlock | v5s-LCBlock2 | v5s-LCB3-chunk2 |
|---|---|---|---|---|
| epoch10 | 0.20609 | 0.20474 | 0.20699 | 0.21211 |
| epoch20 | 0.24671 | 0.24611 | 0.24932 | 0.25482 |
| epoch30 | 0.2564 | 0.25629 | 0.2584 | 0.26505 |
| epoch50 | 0.27553 | 0.27647 | 0.27931 | 0.28598, |
| epoch80 | 0.30649 | 0.30654 | 0.30737 | |
| epoch150 | 0.33338 | 0.33523 | ---- |
v5s-lite-g
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091764, 0.083605, 0.083667, 0.016046, 0.056931, 0.0041556, 0.0014131, 0.07822, 0.0657, 0.0787, 0.0033297, 0.0033297, 0.0700321, 0.073686, 0.083846, 0.075502, 0.32996, 0.057096, 0.020941, 0.0088895, 0.069293, 0.063517, 0.068443, 0.0066629, 0.0066629, 0.0400322, 0.06799, 0.082278, 0.063985, 0.3211, 0.13373, 0.072504, 0.03121, 0.063444, 0.062623, 0.054571, 0.0099954, 0.0099954, 0.0100313, 0.063771, 0.080539, 0.053533, 0.33994, 0.21106, 0.14836, 0.070977, 0.058648, 0.060657, 0.044437, 0.0099978, 0.0099978, 0.00999784, 0.061017, 0.077907, 0.048235, 0.34807, 0.244, 0.19708, 0.10435, 0.056184, 0.058426, 0.039842, 0.0099978, 0.0099978, 0.00999785, 0.059494, 0.077005, 0.044746, 0.39311, 0.27296, 0.24094, 0.13153, 0.054184, 0.05767, 0.036521, 0.0099961, 0.0099961, 0.00999616, 0.058339, 0.076628, 0.042245, 0.42702, 0.29912, 0.278, 0.15608, 0.052886, 0.056604, 0.034162, 0.0099938, 0.0099938, 0.00999387, 0.05765, 0.076134, 0.040563, 0.43897, 0.32715, 0.30912, 0.17754, 0.051631, 0.055954, 0.032001, 0.0099911, 0.0099911, 0.00999118, 0.056604, 0.07506, 0.039379, 0.46377, 0.34762, 0.33084, 0.19195, 0.050889, 0.055378, 0.030631, 0.0099879, 0.0099879, 0.00998799, 0.056356, 0.075845, 0.03818, 0.51648, 0.34182, 0.34928, 0.2052, 0.050079, 0.054832, 0.029381, 0.0099842, 0.0099842, 0.009984210, 0.055955, 0.075936, 0.037408, 0.49384, 0.37324, 0.36567, 0.21598, 0.049515, 0.054541, 0.028365, 0.00998, 0.00998, 0.0099811, 0.055443, 0.07421, 0.036685, 0.50411, 0.38051, 0.37694, 0.22426, 0.049056, 0.054128, 0.027581, 0.0099753, 0.0099753, 0.009975312, 0.055402, 0.074553, 0.035941, 0.53431, 0.37807, 0.38842, 0.23277, 0.048627, 0.053846, 0.027027, 0.0099702, 0.0099702, 0.009970213, 0.054766, 0.074203, 0.035406, 0.52697, 0.39371, 0.39624, 0.2389, 0.048288, 0.05367, 0.026508, 0.0099645, 0.0099645, 0.009964514, 0.054625, 0.073913, 0.034909, 0.55272, 0.38366, 0.40125, 0.24303, 0.048002, 0.053492, 0.026123, 0.0099584, 0.0099584, 0.009958415, 0.054317, 0.073803, 0.034637, 0.54119, 0.39693, 0.40664, 0.24744, 0.047798, 0.053324, 0.025797, 0.0099517, 0.0099517, 0.009951716, 0.054204, 0.073825, 0.034358, 0.54239, 0.40287, 0.41212, 0.25056, 0.047653, 0.053182, 0.025574, 0.0099446, 0.0099446, 0.009944617, 0.053936, 0.072659, 0.03417, 0.53188, 0.41191, 0.416, 0.25368, 0.047536, 0.053067, 0.025362, 0.009937, 0.009937, 0.00993718, 0.0539, 0.073468, 0.033832, 0.57219, 0.3927, 0.41887, 0.25594, 0.047424, 0.052961, 0.025217, 0.0099289, 0.0099289, 0.009928919, 0.053708, 0.072787, 0.033427, 0.57544, 0.39629, 0.42192, 0.25798, 0.04732, 0.052878, 0.025078, 0.0099203, 0.0099203, 0.009920320, 0.05354, 0.072535, 0.033043, 0.57687, 0.39726, 0.42415, 0.25994, 0.047237, 0.052805, 0.024953, 0.0099112, 0.0099112, 0.009911221, 0.053139, 0.072018, 0.032922, 0.56312, 0.40651, 0.42616, 0.26174, 0.047163, 0.052731, 0.024833, 0.0099017, 0.0099017, 0.009901722, 0.05324, 0.072314, 0.032852, 0.56698, 0.40809, 0.42802, 0.26296, 0.047094, 0.052672, 0.024743, 0.0098916, 0.0098916, 0.009891623, 0.052984, 0.071691, 0.032502, 0.5688, 0.40835, 0.42939, 0.26433, 0.047033, 0.052621, 0.024654, 0.0098811, 0.0098811, 0.009881124, 0.053075, 0.072245, 0.032542, 0.55917, 0.41549, 0.43074, 0.26544, 0.046973, 0.052579, 0.024576, 0.0098701, 0.0098701, 0.009870125, 0.052899, 0.073219, 0.032326, 0.57717, 0.40883, 0.43238, 0.26645, 0.046922, 0.052545, 0.024498, 0.0098586, 0.0098586, 0.009858626, 0.052927, 0.072695, 0.032286, 0.56001, 0.41737, 0.43341, 0.26761, 0.046877, 0.052522, 0.024433, 0.0098467, 0.0098467, 0.009846727, 0.052769, 0.072309, 0.032052, 0.55659, 0.42052, 0.43427, 0.2685, 0.046839, 0.052514, 0.024381, 0.0098342, 0.0098342, 0.009834228, 0.052567, 0.071681, 0.031901, 0.58324, 0.40771, 0.43537, 0.26933, 0.046809, 0.052515, 0.024332, 0.0098213, 0.0098213, 0.009821329, 0.052512, 0.071409, 0.031706, 0.55935, 0.4201, 0.4366, 0.27006, 0.04678, 0.052536, 0.024281, 0.0098079, 0.0098079, 0.009807930, 0.05257, 0.072451, 0.031773, 0.60038, 0.40082, 0.4375, 0.27078, 0.046756, 0.052566, 0.024233, 0.0097941, 0.0097941, 0.009794131, 0.052397, 0.072427, 0.031523, 0.59812, 0.40353, 0.43831, 0.27164, 0.046734, 0.052607, 0.024196, 0.0097798, 0.0097798, 0.009779832, 0.052493, 0.072668, 0.03139, 0.5401, 0.43229, 0.43888, 0.27255, 0.046715, 0.052658, 0.024157, 0.009765, 0.009765, 0.00976533, 0.05221, 0.071673, 0.031336, 0.60056, 0.40494, 0.44013, 0.27315, 0.046691, 0.052701, 0.024123, 0.0097497, 0.0097497, 0.009749734, 0.052178, 0.071612, 0.031145, 0.56314, 0.42246, 0.44092, 0.27383, 0.046666, 0.052762, 0.024081, 0.009734, 0.009734, 0.009734v5s-g
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.090561, 0.083676, 0.083902, 0.0058319, 0.047981, 0.0049234, 0.0016317, 0.07946, 0.065755, 0.078896, 0.0033297, 0.0033297, 0.0700321, 0.07336, 0.08333, 0.075127, 0.39751, 0.056787, 0.020281, 0.0077558, 0.070273, 0.063855, 0.067316, 0.0066629, 0.0066629, 0.0400322, 0.06794, 0.082012, 0.063558, 0.33755, 0.13032, 0.071825, 0.031181, 0.063522, 0.062164, 0.053643, 0.0099954, 0.0099954, 0.0100313, 0.063912, 0.079575, 0.0537, 0.35576, 0.18729, 0.14409, 0.070619, 0.059045, 0.059896, 0.044766, 0.0099978, 0.0099978, 0.00999784, 0.061241, 0.077947, 0.048031, 0.39008, 0.22238, 0.19577, 0.099902, 0.056331, 0.058283, 0.039873, 0.0099978, 0.0099978, 0.00999785, 0.059561, 0.07713, 0.044272, 0.39467, 0.27314, 0.24088, 0.13006, 0.054377, 0.057369, 0.036662, 0.0099961, 0.0099961, 0.00999616, 0.058417, 0.076726, 0.042186, 0.44821, 0.29642, 0.28312, 0.15765, 0.052918, 0.056584, 0.03407, 0.0099938, 0.0099938, 0.00999387, 0.057537, 0.075668, 0.04065, 0.44709, 0.32061, 0.3053, 0.17384, 0.051819, 0.05594, 0.032175, 0.0099911, 0.0099911, 0.00999118, 0.05707, 0.075075, 0.039091, 0.48689, 0.32388, 0.32769, 0.18949, 0.0509, 0.05535, 0.030464, 0.0099879, 0.0099879, 0.00998799, 0.056446, 0.075107, 0.038153, 0.46891, 0.3638, 0.3453, 0.20173, 0.050224, 0.054859, 0.029229, 0.0099842, 0.0099842, 0.009984210, 0.05599, 0.074498, 0.037155, 0.52217, 0.35487, 0.36348, 0.21338, 0.049652, 0.054643, 0.0284, 0.00998, 0.00998, 0.0099811, 0.055954, 0.074994, 0.036722, 0.49573, 0.37614, 0.3719, 0.22167, 0.049202, 0.054211, 0.027769, 0.0099753, 0.0099753, 0.009975312, 0.055413, 0.074568, 0.035754, 0.49957, 0.38414, 0.38323, 0.22898, 0.048783, 0.053969, 0.027122, 0.0099702, 0.0099702, 0.009970213, 0.054981, 0.07344, 0.035519, 0.52262, 0.38747, 0.39345, 0.2357, 0.048482, 0.053742, 0.026598, 0.0099645, 0.0099645, 0.009964514, 0.054688, 0.072956, 0.035128, 0.52054, 0.39699, 0.39944, 0.24077, 0.048217, 0.05351, 0.026196, 0.0099584, 0.0099584, 0.009958415, 0.054571, 0.073867, 0.034747, 0.56586, 0.38297, 0.40535, 0.24487, 0.047954, 0.05336, 0.025864, 0.0099517, 0.0099517, 0.009951716, 0.054332, 0.073118, 0.034194, 0.58583, 0.38081, 0.41132, 0.24901, 0.047765, 0.053222, 0.025573, 0.0099446, 0.0099446, 0.009944617, 0.054319, 0.073307, 0.034254, 0.5522, 0.40069, 0.41508, 0.2516, 0.047618, 0.05312, 0.02536, 0.009937, 0.009937, 0.00993718, 0.053818, 0.073281, 0.033555, 0.55216, 0.40321, 0.41761, 0.25416, 0.047509, 0.053005, 0.025187, 0.0099289, 0.0099289, 0.009928919, 0.053636, 0.072146, 0.033614, 0.58506, 0.39111, 0.42018, 0.25602, 0.047416, 0.052914, 0.025055, 0.0099203, 0.0099203, 0.009920320, 0.053749, 0.072678, 0.033477, 0.58027, 0.39386, 0.42221, 0.25771, 0.047338, 0.052827, 0.024943, 0.0099112, 0.0099112, 0.009911221, 0.053523, 0.07243, 0.03298, 0.56371, 0.40463, 0.42434, 0.25931, 0.047264, 0.052753, 0.024834, 0.0099017, 0.0099017, 0.009901722, 0.053351, 0.071941, 0.032575, 0.56743, 0.40402, 0.42539, 0.26088, 0.047201, 0.052685, 0.024746, 0.0098916, 0.0098916, 0.009891623, 0.053263, 0.072275, 0.032751, 0.56791, 0.40374, 0.42676, 0.26203, 0.047139, 0.052629, 0.024666, 0.0098811, 0.0098811, 0.009881124, 0.052973, 0.07208, 0.032323, 0.55382, 0.41207, 0.42797, 0.26304, 0.047087, 0.052588, 0.024598, 0.0098701, 0.0098701, 0.009870125, 0.052886, 0.071604, 0.03219, 0.55647, 0.41262, 0.42945, 0.26401, 0.047041, 0.052566, 0.024535, 0.0098586, 0.0098586, 0.009858626, 0.053032, 0.072313, 0.032136, 0.55879, 0.41282, 0.43074, 0.265, 0.047002, 0.052565, 0.024472, 0.0098467, 0.0098467, 0.009846727, 0.052872, 0.072334, 0.032071, 0.55561, 0.41583, 0.43179, 0.26574, 0.046965, 0.052578, 0.024414, 0.0098342, 0.0098342, 0.009834228, 0.052599, 0.071546, 0.031794, 0.58263, 0.40555, 0.43298, 0.26681, 0.046929, 0.0526, 0.024354, 0.0098213, 0.0098213, 0.009821329, 0.052618, 0.071915, 0.031572, 0.55361, 0.42016, 0.43356, 0.26752, 0.046899, 0.052632, 0.024299, 0.0098079, 0.0098079, 0.009807930, 0.05259, 0.071716, 0.031395, 0.55701, 0.42011, 0.43425, 0.26827, 0.046869, 0.052684, 0.024257, 0.0097941, 0.0097941, 0.009794131, 0.052528, 0.071275, 0.031778, 0.55873, 0.42122, 0.43573, 0.26875, 0.046841, 0.052745, 0.024209, 0.0097798, 0.0097798, 0.009779832, 0.052462, 0.072346, 0.031427, 0.56271, 0.4202, 0.43623, 0.269, 0.046816, 0.052822, 0.02416, 0.009765, 0.009765, 0.009765v5m-bs128
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.088815, 0.083758, 0.083028, 0.10967, 0.060776, 0.0084766, 0.0028998, 0.07658, 0.065096, 0.075192, 0.0033297, 0.0033297, 0.0700321, 0.07153, 0.082675, 0.069671, 0.25921, 0.12556, 0.050926, 0.021388, 0.067456, 0.063615, 0.058973, 0.0066629, 0.0066629, 0.0400322, 0.065678, 0.079942, 0.056423, 0.28735, 0.19372, 0.13283, 0.059436, 0.060568, 0.061682, 0.046271, 0.0099954, 0.0099954, 0.0100313, 0.061338, 0.078504, 0.04723, 0.40063, 0.24958, 0.22877, 0.11608, 0.055906, 0.058371, 0.037091, 0.0099978, 0.0099978, 0.00999784, 0.058455, 0.076397, 0.042069, 0.42689, 0.30947, 0.28819, 0.15312, 0.053267, 0.057035, 0.032819, 0.0099978, 0.0099978, 0.00999785, 0.056665, 0.075084, 0.038737, 0.48373, 0.33178, 0.32958, 0.18127, 0.051449, 0.05598, 0.03003, 0.0099961, 0.0099961, 0.00999616, 0.055591, 0.075298, 0.036506, 0.49513, 0.36799, 0.36491, 0.2067, 0.049934, 0.054871, 0.027925, 0.0099938, 0.0099938, 0.00999387, 0.054502, 0.073199, 0.035426, 0.49643, 0.39808, 0.38942, 0.22583, 0.048767, 0.054358, 0.026238, 0.0099911, 0.0099911, 0.00999118, 0.053876, 0.07238, 0.033299, 0.52135, 0.41381, 0.41562, 0.24501, 0.04781, 0.053518, 0.024843, 0.0099879, 0.0099879, 0.00998799, 0.053236, 0.072389, 0.032481, 0.54612, 0.41269, 0.43074, 0.25796, 0.046989, 0.053086, 0.023894, 0.0099842, 0.0099842, 0.009984210, 0.052832, 0.07241, 0.031733, 0.55228, 0.43234, 0.44671, 0.26953, 0.046382, 0.052618, 0.023047, 0.00998, 0.00998, 0.0099811, 0.05282, 0.074112, 0.031097, 0.5533, 0.44669, 0.46024, 0.28065, 0.045857, 0.052293, 0.022248, 0.0099753, 0.0099753, 0.009975312, 0.052155, 0.071912, 0.030904, 0.55584, 0.45631, 0.46781, 0.28701, 0.045427, 0.051986, 0.021778, 0.0099702, 0.0099702, 0.009970213, 0.051747, 0.072218, 0.03014, 0.60159, 0.44424, 0.47684, 0.29523, 0.045071, 0.051834, 0.021251, 0.0099645, 0.0099645, 0.009964514, 0.051199, 0.071139, 0.029753, 0.60888, 0.44745, 0.4834, 0.30013, 0.044758, 0.051615, 0.020943, 0.0099584, 0.0099584, 0.009958415, 0.051116, 0.071044, 0.029682, 0.61669, 0.45095, 0.48788, 0.30411, 0.044482, 0.051461, 0.020708, 0.0099517, 0.0099517, 0.009951716, 0.051165, 0.071407, 0.029131, 0.58867, 0.46949, 0.49298, 0.30788, 0.044266, 0.051328, 0.020452, 0.0099446, 0.0099446, 0.009944617, 0.050888, 0.070982, 0.029018, 0.60247, 0.46829, 0.49698, 0.3109, 0.044097, 0.051225, 0.020252, 0.009937, 0.009937, 0.00993718, 0.050531, 0.070728, 0.028839, 0.62038, 0.46144, 0.49886, 0.31304, 0.04396, 0.051144, 0.020106, 0.0099289, 0.0099289, 0.009928919, 0.050503, 0.070549, 0.028389, 0.61684, 0.4655, 0.50101, 0.31475, 0.043849, 0.051041, 0.019979, 0.0099203, 0.0099203, 0.009920320, 0.050645, 0.070725, 0.028245, 0.60357, 0.47478, 0.5033, 0.31661, 0.043755, 0.050947, 0.019874, 0.0099112, 0.0099112, 0.009911221, 0.050031, 0.069934, 0.027804, 0.60496, 0.47581, 0.50527, 0.31852, 0.043667, 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0.023495, 0.66224, 0.54308, 0.58145, 0.38531, 0.040482, 0.048251, 0.016126, 0.0084404, 0.0084404, 0.0084404yolov5s
epoch, box_loss obj_loss, cls_loss, precision,recall, mAP_0.5, 0.5:0.95, box_loss,obj_loss, cls_loss, x/lr0, x/lr1, x/lr20/299 0.514G 0.08983 0.08378 0.0827 0.2563 35 640 0.1194 0.04001 0.004798 0.001399 0.07812 0.06498 0.0771/299 12.1G 0.0736 0.08291 0.07077 0.2273 43 640 0.3335 0.09528 0.04203 0.01542 0.07008 0.06207 0.060962/299 13.9G 0.06825 0.08104 0.05821 0.2075 25 640 0.3315 0.1591 0.09668 0.03747 0.06493 0.06124 0.049353/299 13.9G 0.06433 0.07907 0.04916 0.1926 41 640 0.3415 0.2254 0.1707 0.07781 0.05992 0.05944 0.040794/299 13.9G 0.06164 0.07765 0.04405 0.1833 17 640 0.4311 0.249 0.2297 0.1112 0.05726 0.05856 0.036435/299 13.9G 0.06012 0.07706 0.04113 0.1783 36 640 0.4069 0.2922 0.2669 0.1348 0.05562 0.05759 0.033446/299 13.9G 0.05902 0.07624 0.03907 0.1743 71 640 0.4815 0.3003 0.3001 0.1566 0.05415 0.05684 0.030967/299 13.9G 0.05829 0.07524 0.03744 0.171 74 640 0.4619 0.3395 0.3239 0.1722 0.05314 0.05624 0.029398/299 13.9G 0.05778 0.07568 0.03638 0.1698 80 640 0.5126 0.3358 0.3444 0.1866 0.05225 0.05577 0.028189/299 13.9G 0.05721 0.07519 0.0356 0.168 46 640 0.521 0.3554 0.3634 0.1998 0.05158 0.05533 0.02710/299 13.9G 0.05662 0.07448 0.03487 0.166 32 640 0.5044 0.3743 0.3757 0.2083 0.05103 0.05493 0.0262311/299 13.9G 0.05629 0.07418 0.03408 0.1646 75 640 0.517 0.3836 0.3881 0.2169 0.05059 0.05473 0.0255612/299 13.9G 0.05601 0.07415 0.03357 0.1637 65 640 0.5281 0.3862 0.3939 0.2217 0.05021 0.05443 0.0250313/299 13.9G 0.05573 0.0742 0.03318 0.1631 63 640 0.5521 0.3839 0.401 0.2279 0.04981 0.05421 0.0245314/299 13.9G 0.05541 0.0737 0.03265 0.1618 37 640 0.5591 0.391 0.4085 0.2338 0.04954 0.05401 0.024115/299 13.9G 0.05519 0.07384 0.03234 0.1614 28 640 0.5382 0.4074 0.413 0.2376 0.04932 0.05388 0.0238216/299 13.9G 0.05492 0.07396 0.03209 0.161 42 640 0.5667 0.3987 0.4175 0.2409 0.04913 0.05374 0.023617/299 13.9G 0.05482 0.07353 0.0319 0.1602 43 640 0.5424 0.4097 0.4211 0.2431 0.04898 0.05364 0.0234418/299 13.9G 0.05465 0.07341 0.0314 0.1595 38 640 0.5381 0.4128 0.4235 0.2455 0.04884 0.05354 0.0232819/299 13.9G 0.05451 0.07297 0.03113 0.1586 81 640 0.5598 0.4054 0.4264 0.2473 0.04874 0.05344 0.0231420/299 13.9G 0.05443 0.07332 0.03108 0.1588 95 640 0.5749 0.3995 0.4289 0.2495 0.04866 0.05337 0.0230321/299 13.9G 0.05414 0.0727 0.03092 0.1578 26 640 0.5735 0.4018 0.4306 0.2506 0.04859 0.0533 0.0229522/299 13.9G 0.05417 0.0729 0.0308 0.1579 76 640 0.5559 0.4127 0.4322 0.2518 0.04853 0.05324 0.0228723/299 13.9G 0.05401 0.0726 0.03051 0.1571 97 640 0.5797 0.4016 0.433 0.253 0.04847 0.05319 0.0228224/299 13.9G 0.05376 0.07268 0.03013 0.1566 77 640 0.5843 0.4011 0.4338 0.2538 0.04843 0.05315 0.0227725/299 13.9G 0.05367 0.07225 0.03033 0.1563 33 640 0.5896 0.4006 0.4349 0.2547 0.04838 0.05311 0.0227226/299 13.9G 0.05358 0.07266 0.02993 0.1562 62 640 0.5885 0.4028 0.4363 0.2554 0.04834 0.05309 0.0226727/299 13.9G 0.05354 0.07231 0.03007 0.1559 80 640 0.5864 0.4041 0.4371 0.2563 0.04831 0.05307 0.0226328/299 13.9G 0.05331 0.07255 0.02963 0.1555 62 640 0.5884 0.4063 0.4386 0.2571 0.04827 0.05306 0.0225929/299 13.9G 0.05326 0.07198 0.02965 0.1549 66 640 0.5837 0.4108 0.4395 0.2579 0.04824 0.05306 0.0225530/299 13.9G 0.05322 0.07234 0.02951 0.1551 54 640 0.5867 0.4096 0.4399 0.2587 0.04821 0.05306 0.0225231/299 13.9G 0.05315 0.07232 0.02951 0.155 46 640 0.5665 0.4219 0.4407 0.259 0.04818 0.05307 0.0224832/299 13.9G 0.05315 0.07247 0.02926 0.1549 29 640 0.5637 0.4239 0.4418 0.2596 0.04815 0.05309 0.0224433/299 13.9G 0.05295 0.07147 0.02943 0.1538 26 640 0.5673 0.4228 0.443 0.2605 0.04811 0.0531 0.0224134/299 13.9G 0.05298 0.07178 0.02915 0.1539 37 640 0.5634 0.426 0.4439 0.2611 0.04808 0.05311 0.0223735/299 13.9G 0.05283 0.07181 0.02887 0.1535 34 640 0.5717 0.4236 0.4453 0.2618 0.04804 0.05311 0.0223336/299 13.9G 0.05278 0.07196 0.02874 0.1535 73 640 0.5685 0.426 0.4459 0.2625 0.048 0.05312 0.0222837/299 13.9G 0.05267 0.07162 0.0289 0.1532 103 640 0.5722 0.425 0.4473 0.2634 0.04796 0.05313 0.0222238/299 13.9G 0.05264 0.07175 0.02878 0.1532 18 640 0.5657 0.4295 0.4478 0.264 0.04792 0.05312 0.0221739/299 13.9G 0.05255 0.07183 0.0286 0.153 58 640 0.5646 0.4302 0.4492 0.2649 0.04788 0.05312 0.0221240/299 13.9G 0.05259 0.07194 0.02856 0.1531 86 640 0.5832 0.4236 0.4505 0.2658 0.04783 0.0531 0.0220541/299 13.9G 0.05239 0.07185 0.02851 0.1527 36 640 0.5801 0.4264 0.4518 0.2667 0.04778 0.05308 0.0219942/299 13.9G 0.05235 0.07162 0.02835 0.1523 73 640 0.5863 0.4254 0.4527 0.2675 0.04773 0.05306 0.0219243/299 13.9G 0.05235 0.07114 0.02857 0.1521 30 640 0.5815 0.4291 0.4545 0.2686 0.04767 0.05302 0.0218544/299 13.9G 0.0523 0.07158 0.02833 0.1522 23 640 0.5914 0.4252 0.4554 0.2698 0.04761 0.05299 0.0217845/299 13.9G 0.05239 0.07187 0.02841 0.1527 89 640 0.587 0.4306 0.4572 0.2711 0.04755 0.05295 0.021746/299 13.9G 0.05219 0.07143 0.02829 0.1519 67 640 0.5747 0.4384 0.4589 0.2721 0.04749 0.05291 0.0216347/299 13.9G 0.05213 0.0717 0.02804 0.1519 54 640 0.5909 0.4336 0.4604 0.2732 0.04743 0.05287 0.0215648/299 13.9G 0.05219 0.07187 0.02816 0.1522 47 640 0.5879 0.4372 0.4621 0.2746 0.04736 0.05282 0.0214849/299 13.9G 0.05191 0.0712 0.02806 0.1512 86 640 0.5875 0.4391 0.4636 0.2761 0.04729 0.05276 0.0214150/299 13.9G 0.05195 0.07123 0.02797 0.1512 55 640 0.5889 0.4404 0.4648 0.2773 0.04723 0.05271 0.0213351/299 13.9G 0.05188 0.07089 0.02786 0.1506 104 640 0.6019 0.4355 0.4664 0.2783 0.04716 0.05265 0.0212652/299 13.9G 0.0519 0.07134 0.02792 0.1512 48 640 0.5904 0.4421 0.4674 0.2795 0.04709 0.05261 0.0211953/299 13.9G 0.05184 0.0709 0.02775 0.1505 45 640 0.6038 0.4361 0.4684 0.2806 0.04703 0.05255 0.0211154/299 13.9G 0.05182 0.07142 0.02777 0.151 61 640 0.5985 0.4413 0.4701 0.2815 0.04697 0.0525 0.0210455/299 13.9G 0.05179 0.07121 0.02773 0.1507 56 640 0.5998 0.443 0.4713 0.2828 0.04691 0.05243 0.0209656/299 13.9G 0.05177 0.07102 0.02769 0.1505 51 640 0.6019 0.4418 0.4721 0.2842 0.04684 0.05238 0.020957/299 13.9G 0.05174 0.0713 0.02759 0.1506 60 640 0.62 0.4344 0.4737 0.285 0.04678 0.05232 0.0208358/299 13.9G 0.05167 0.07133 0.02746 0.1505 31 640 0.6075 0.4405 0.4753 0.2858 0.04673 0.05227 0.0207759/299 13.9G 0.05163 0.07157 0.02744 0.1506 84 640 0.5966 0.4474 0.4763 0.287 0.04667 0.05222 0.020760/299 13.9G 0.05152 0.07094 0.0274 0.1499 86 640 0.5997 0.4486 0.4776 0.2879 0.04662 0.05217 0.0206461/299 13.9G 0.05151 0.0712 0.02744 0.1501 72 640 0.6265 0.4381 0.4787 0.2891 0.04657 0.05212 0.0205862/299 13.9G 0.05158 0.07181 0.02733 0.1507 35 640 0.6248 0.4403 0.4802 0.29 0.04651 0.05207 0.0205263/299 13.9G 0.05141 0.07083 0.02729 0.1495 17 640 0.6215 0.4429 0.4811 0.2912 0.04646 0.05201 0.0204764/299 13.9G 0.05142 0.07106 0.02736 0.1498 61 640 0.6253 0.4408 0.482 0.2921 0.0464 0.05196 0.020465/299 13.9G 0.05138 0.07059 0.02712 0.1491 35 640 0.5948 0.4569 0.4831 0.2932 0.04635 0.05191 0.0203566/299 13.9G 0.05139 0.07051 0.02736 0.1493 73 640 0.6007 0.4564 0.4844 0.2939 0.0463 0.05186 0.020367/299 13.9G 0.05125 0.07068 0.02727 0.1492 19 640 0.6065 0.4557 0.4855 0.2951 0.04624 0.05182 0.0202468/299 13.9G 0.05118 0.07086 0.02705 0.1491 27 640 0.6141 0.4528 0.4864 0.296 0.0462 0.05178 0.0201869/299 13.9G 0.05119 0.07093 0.02711 0.1492 38 640 0.6169 0.4514 0.4878 0.2965 0.04615 0.05174 0.0201470/299 13.9G 0.05123 0.07064 0.02716 0.149 42 640 0.6163 0.4524 0.4887 0.297 0.0461 0.05169 0.0200871/299 13.9G 0.0512 0.07054 0.02688 0.1486 26 640 0.6059 0.4585 0.4896 0.2979 0.04606 0.05164 0.0200272/299 13.9G 0.05109 0.07064 0.02706 0.1488 43 640 0.6122 0.4571 0.4907 0.2987 0.04601 0.05159 0.0199773/299 13.9G 0.05119 0.07036 0.02696 0.1485 45 640 0.6171 0.4542 0.4911 0.2997 0.04596 0.05155 0.0199274/299 13.9G 0.05109 0.07071 0.02697 0.1488 87 640 0.6161 0.4564 0.4923 0.3004 0.04592 0.05151 0.0198775/299 13.9G 0.05106 0.07082 0.0268 0.1487 93 640 0.6187 0.4561 0.493 0.3008 0.04588 0.05147 0.0198276/299 13.9G 0.051 0.07073 0.02673 0.1485 54 640 0.613 0.459 0.4938 0.3015 0.04584 0.05142 0.0197777/299 13.9G 0.05089 0.07088 0.02671 0.1485 26 640 0.6137 0.4607 0.4949 0.3022 0.04579 0.05138 0.0197278/299 13.9G 0.05101 0.07076 0.02676 0.1485 44 640 0.5912 0.4766 0.496 0.3031 0.04575 0.05135 0.0196879/299 13.9G 0.05083 0.07027 0.02652 0.1476 65 640 0.5986 0.4736 0.4969 0.3036 0.04571 0.05131 0.0196580/299 13.9G 0.05089 0.07033 0.02673 0.148 28 640 0.6124 0.4689 0.498 0.3045 0.04568 0.05129 0.019681/299 13.9G 0.05093 0.0707 0.02661 0.1482 40 640 0.6135 0.4679 0.4988 0.3051 0.04565 0.05125 0.0195782/299 13.9G 0.05075 0.07079 0.02667 0.1482 34 640 0.6106 0.47 0.499 0.3056 0.04561 0.05122 0.0195383/299 13.9G 0.05073 0.07052 0.02658 0.1478 25 640 0.5994 0.4765 0.5001 0.3062 0.04558 0.05119 0.0194984/299 13.9G 0.05066 0.07025 0.02652 0.1474 43 640 0.6155 0.4676 0.4997 0.3064 0.04555 0.05116 0.0194585/299 13.9G 0.05064 0.07047 0.0265 0.1476 53 640 0.6222 0.4661 0.501 0.307 0.04552 0.05113 0.0194186/299 13.9G 0.05073 0.07078 0.02638 0.1479 44 640 0.6253 0.4657 0.5019 0.3077 0.04549 0.05111 0.0193787/299 13.9G 0.05085 0.07053 0.02638 0.1478 48 640 0.6244 0.4669 0.5038 0.3089 0.04546 0.05108 0.0193388/299 13.9G 0.05058 0.07019 0.02623 0.147 26 640 0.6253 0.467 0.5047 0.3095 0.04543 0.05106 0.0192989/299 13.9G 0.05057 0.07035 0.02644 0.1474 36 640 0.6291 0.4643 0.5052 0.3098 0.0454 0.05104 0.0192590/299 13.9G 0.05063 0.07044 0.02618 0.1473 45 640 0.6227 0.4677 0.5058 0.3105 0.04538 0.05102 0.0192191/299 13.9G 0.05046 0.07021 0.02621 0.1469 37 640 0.6151 0.4731 0.5067 0.3111 0.04535 0.05099 0.0191792/299 13.9G 0.05044 0.07041 0.0262 0.1471 81 640 0.6155 0.4746 0.508 0.312 0.04532 0.05097 0.0191393/299 13.9G 0.05041 0.06993 0.02607 0.1464 62 640 0.6212 0.4727 0.5085 0.3125 0.0453 0.05095 0.0190994/299 13.9G 0.05054 0.07044 0.02621 0.1472 44 640 0.6101 0.4793 0.5092 0.3129 0.04528 0.05093 0.0190695/299 13.9G 0.05033 0.0698 0.02606 0.1462 39 640 0.6045 0.4828 0.5093 0.3131 0.04525 0.05091 0.0190396/299 13.9G 0.05044 0.07008 0.02611 0.1466 81 640 0.6158 0.478 0.51 0.3134 0.04523 0.05089 0.01997/299 13.9G 0.05042 0.07099 0.02594 0.1473 56 640 0.612 0.4809 0.51 0.3133 0.0452 0.05087 0.0189698/299 13.9G 0.05024 0.06972 0.02603 0.146 48 640 0.63 0.4728 0.5108 0.3141 0.04518 0.05086 0.0189399/299 13.9G 0.0503 0.07 0.0261 0.1464 60 640 0.6109 0.4824 0.5115 0.3143 0.04515 0.05084 0.01888100/299 13.9G 0.05016 0.06984 0.02592 0.1459 50 640 0.6407 0.4697 0.5124 0.315 0.04513 0.05082 0.01885101/299 13.9G 0.0503 0.07008 0.02591 0.1463 63 640 0.6279 0.4771 0.5128 0.3154 0.0451 0.05081 0.01882102/299 13.9G 0.05026 0.06997 0.02589 0.1461 59 640 0.629 0.4772 0.5137 0.3158 0.04508 0.05079 0.01879103/299 13.9G 0.05017 0.07046 0.02587 0.1465 30 640 0.638 0.4745 0.5142 0.316 0.04506 0.05077 0.01877104/299 13.9G 0.05025 0.07051 0.02572 0.1465 53 640 0.6317 0.4763 0.5143 0.3166 0.04503 0.05075 0.01874105/299 13.9G 0.05015 0.06961 0.02557 0.1453 61 640 0.6316 0.4775 0.5152 0.3175 0.045 0.05073 0.01871106/299 13.9G 0.0501 0.07019 0.02576 0.146 30 640 0.6228 0.4834 0.516 0.3177 0.04498 0.05071 0.01869107/299 13.9G 0.0502 0.06981 0.02575 0.1458 17 640 0.6276 0.4798 0.5162 0.3182 0.04496 0.0507 0.01868108/299 13.9G 0.05004 0.07002 0.02569 0.1457 68 640 0.6091 0.4907 0.5164 0.3184 0.04493 0.05069 0.01867109/299 13.9G 0.04992 0.06985 0.0255 0.1453 56 640 0.6029 0.4943 0.5166 0.3192 0.04491 0.05067 0.01864110/299 13.9G 0.05001 0.06975 0.02565 0.1454 35 640 0.6214 0.4847 0.5173 0.3196 0.04489 0.05066 0.01862111/299 13.9G 0.05002 0.0702 0.02554 0.1458 41 640 0.6133 0.4906 0.5177 0.3197 0.04487 0.05065 0.0186112/299 13.9G 0.04995 0.06921 0.02565 0.1448 41 640 0.6152 0.4897 0.5187 0.3206 0.04485 0.05064 0.01859113/299 13.9G 0.0499 0.06982 0.02538 0.1451 71 640 0.6226 0.487 0.5191 0.3205 0.04482 0.05063 0.01856114/299 13.9G 0.04982 0.06954 0.02548 0.1448 81 640 0.6252 0.4857 0.5195 0.3208 0.0448 0.05062 0.01853115/299 13.9G 0.04997 0.06978 0.02545 0.1452 76 640 0.6257 0.486 0.5197 0.321 0.04478 0.05061 0.01851116/299 13.9G 0.04984 0.06965 0.02557 0.1451 42 640 0.6258 0.487 0.5206 0.3213 0.04476 0.05061 0.01849117/299 13.9G 0.04983 0.07003 0.02536 0.1452 31 640 0.6384 0.4796 0.5208 0.322 0.04475 0.05059 0.01848118/299 13.9G 0.04978 0.0695 0.02539 0.1447 38 640 0.6274 0.4866 0.5213 0.3226 0.04473 0.05058 0.01846119/299 13.9G 0.04963 0.06953 0.02525 0.1444 30 640 0.6304 0.4853 0.5215 0.323 0.04471 0.05058 0.01843120/299 13.9G 0.04971 0.06967 0.02537 0.1448 29 640 0.6465 0.4776 0.5217 0.3234 0.0447 0.05057 0.01841121/299 13.9G 0.04979 0.06958 0.02512 0.1445 37 640 0.6441 0.4775 0.5217 0.3235 0.04469 0.05056 0.0184122/299 13.9G 0.04993 0.06982 0.02532 0.1451 57 640 0.6404 0.479 0.5218 0.3239 0.04467 0.05055 0.01838123/299 13.9G 0.04958 0.06963 0.02497 0.1442 64 640 0.6192 0.4901 0.5222 0.324 0.04465 0.05054 0.01836124/299 13.9G 0.04956 0.06996 0.02511 0.1446 70 640 0.619 0.4912 0.5229 0.3244 0.04464 0.05053 0.01834125/299 13.9G 0.04962 0.06923 0.02494 0.1438 38 640 0.6195 0.4927 0.5235 0.3247 0.04462 0.05052 0.01833126/299 13.9G 0.04964 0.06938 0.02518 0.1442 36 640 0.6149 0.4959 0.5243 0.3252 0.0446 0.05051 0.01831127/299 13.9G 0.04964 0.06994 0.02506 0.1446 19 640 0.614 0.4964 0.5246 0.3258 0.04459 0.0505 0.01829128/299 13.9G 0.04955 0.06917 0.02492 0.1436 19 640 0.6218 0.4921 0.5255 0.3263 0.04457 0.0505 0.01825129/299 13.9G 0.04957 0.06944 0.02487 0.1439 79 640 0.6164 0.4975 0.5264 0.3263 0.04455 0.05049 0.01823130/299 13.9G 0.04945 0.06973 0.02481 0.144 62 640 0.6147 0.4984 0.5266 0.3269 0.04453 0.05048 0.01821131/299 13.9G 0.04948 0.0693 0.02493 0.1437 34 640 0.6257 0.4916 0.5269 0.3269 0.04452 0.05047 0.01819132/299 13.9G 0.04933 0.06903 0.0247 0.1431 94 640 0.6251 0.4918 0.5273 0.3272 0.0445 0.05046 0.01818133/299 13.9G 0.04933 0.06906 0.0247 0.1431 73 640 0.6171 0.4963 0.5274 0.3281 0.04447 0.05045 0.01815134/299 13.9G 0.04941 0.06976 0.02479 0.144 70 640 0.6161 0.4967 0.5276 0.3285 0.04445 0.05043 0.01813135/299 13.9G 0.04931 0.06943 0.02469 0.1434 49 640 0.627 0.4911 0.5279 0.3288 0.04443 0.05042 0.01811136/299 13.9G 0.04944 0.0695 0.02473 0.1437 72 640 0.6436 0.4825 0.5282 0.3291 0.04441 0.0504 0.01809137/299 13.9G 0.04926 0.06901 0.02465 0.1429 51 640 0.645 0.4823 0.5287 0.3296 0.0444 0.05038 0.01808138/299 13.9G 0.04932 0.06938 0.02476 0.1435 60 640 0.6409 0.4852 0.5289 0.3298 0.04439 0.05037 0.01806139/299 13.9G 0.04929 0.0694 0.02463 0.1433 60 640 0.6259 0.4928 0.5297 0.3298 0.04437 0.05034 0.01804140/299 13.9G 0.04934 0.06922 0.02455 0.1431 120 640 0.6344 0.4888 0.5301 0.3303 0.04435 0.05033 0.01802141/299 13.9G 0.04924 0.06931 0.02464 0.1432 35 640 0.6347 0.4891 0.5306 0.3306 0.04433 0.05032 0.01801142/299 13.9G 0.04906 0.06904 0.02447 0.1426 58 640 0.6469 0.4829 0.5308 0.3312 0.04432 0.05031 0.01799143/299 13.9G 0.04917 0.06905 0.02435 0.1426 81 640 0.625 0.4941 0.531 0.3315 0.04431 0.05029 0.01798144/299 13.9G 0.04919 0.06887 0.02427 0.1423 45 640 0.6417 0.4864 0.5316 0.332 0.04429 0.05029 0.01796145/299 13.9G 0.04917 0.06891 0.0244 0.1425 28 640 0.646 0.4843 0.5314 0.332 0.04428 0.05027 0.01794146/299 13.9G 0.04912 0.06936 0.02445 0.1429 51 640 0.6232 0.4977 0.5316 0.3322 0.04427 0.05026 0.01794147/299 13.9G 0.04904 0.06917 0.02426 0.1425 22 640 0.6438 0.4869 0.5318 0.3323 0.04426 0.05025 0.01793148/299 13.9G 0.04906 0.06916 0.0244 0.1426 39 640 0.6556 0.4817 0.5322 0.3323 0.04425 0.05024 0.01792149/299 13.9G 0.04902 0.06903 0.02424 0.1423 38 640 0.6462 0.4871 0.5324 0.3323 0.04423 0.05023 0.01791150/299 13.9G 0.04899 0.06909 0.02422 0.1423 46 640 0.6337 0.494 0.5325 0.3324 0.04422 0.05022 0.01789151/299 13.9G 0.04903 0.06961 0.02415 0.1428 41 640 0.6375 0.4921 0.5327 0.3325 0.0442 0.05022 0.01788152/299 13.9G 0.04908 0.06921 0.02427 0.1426 47 640 0.6348 0.4936 0.5329 0.3327 0.0442 0.05021 0.01787153/299 13.9G 0.04887 0.06909 0.02405 0.142 106 640 0.6533 0.483 0.5331 0.3333 0.04419 0.0502 0.01786154/299 13.9G 0.04895 0.06891 0.02404 0.1419 65 640 0.6428 0.4896 0.5338 0.3338 0.04417 0.05019 0.01784155/299 13.9G 0.04889 0.06886 0.02401 0.1418 51 640 0.6619 0.4801 0.5342 0.3343 0.04415 0.05017 0.01783156/299 13.9G 0.04885 0.06849 0.02394 0.1413 60 640 0.6539 0.4861 0.535 0.3348 0.04414 0.05016 0.01782157/299 13.9G 0.04871 0.06865 0.02396 0.1413 52 640 0.6521 0.4879 0.5351 0.3352 0.04413 0.05016 0.0178158/299 13.9G 0.04867 0.06844 0.02382 0.1409 29 640 0.6486 0.4892 0.5349 0.3354 0.04412 0.05015 0.01779159/299 13.9G 0.04879 0.06946 0.02388 0.1421 50 640 0.6634 0.4824 0.5354 0.3355 0.04411 0.05014 0.01778160/299 13.9G 0.04865 0.06895 0.02383 0.1414 34 640 0.6586 0.4849 0.5354 0.3357 0.0441 0.05013 0.01777161/299 13.9G 0.04873 0.06927 0.02371 0.1417 52 640 0.6594 0.485 0.5357 0.3357 0.04409 0.05013 0.01775162/299 13.9G 0.04861 0.06876 0.02369 0.141 33 640 0.6413 0.4939 0.5354 0.3362 0.04408 0.05013 0.01773163/299 13.9G 0.04857 0.06862 0.02371 0.1409 13 640 0.6578 0.4855 0.5356 0.3363 0.04406 0.05012 0.01771164/299 13.9G 0.04859 0.06874 0.02356 0.1409 48 640 0.6417 0.4947 0.5362 0.3366 0.04405 0.05012 0.01769165/299 13.9G 0.04851 0.06865 0.02355 0.1407 21 640 0.6484 0.4914 0.5369 0.3366 0.04404 0.05011 0.01767166/299 13.9G 0.04848 0.06861 0.02344 0.1405 46 640 0.6631 0.4844 0.5374 0.3368 0.04403 0.0501 0.01766167/299 13.9G 0.04848 0.06861 0.02365 0.1407 33 640 0.6623 0.4858 0.5378 0.337 0.04401 0.05009 0.01765168/299 13.9G 0.04852 0.0687 0.0236 0.1408 29 640 0.6438 0.4965 0.5381 0.3369 0.044 0.05008 0.01764169/299 13.9G 0.0485 0.06875 0.02348 0.1407 48 640 0.6591 0.4891 0.5382 0.337 0.04399 0.05007 0.01763170/299 13.9G 0.0484 0.06853 0.02337 0.1403 37 640 0.6628 0.4871 0.5381 0.3373 0.04398 0.05006 0.01762171/299 13.9G 0.04844 0.06891 0.02338 0.1407 92 640 0.6126 0.5132 0.5384 0.3373 0.04397 0.05005 0.0176172/299 13.9G 0.04844 0.0687 0.02338 0.1405 24 640 0.6146 0.5128 0.538 0.3373 0.04397 0.05005 0.01758repvgg-v5s1
这个部分我将只讲C3-Bottleneck的cv1-cv2进行了替换，其他部分没有替换。效果提升的不是很明显。
-
#batchsize=128 v5s 结构重参数化bottleneck #95M 参数量 FLOPs 22.4
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091325, 0.084251, 0.083019, 0.045864, 0.040693, 0.0064491, 0.0018495, 0.082129, 0.062835, 0.077701, 0.0033297, 0.0033297, 0.0700321, 0.074439, 0.083221, 0.071887, 0.31976, 0.085781, 0.036077, 0.013726, 0.070137, 0.061606, 0.063021, 0.0066629, 0.0066629, 0.0400322, 0.068349, 0.080858, 0.058965, 0.31089, 0.15308, 0.099369, 0.042348, 0.064392, 0.059422, 0.049325, 0.0099954, 0.0099954, 0.0100313, 0.063965, 0.078798, 0.04917, 0.32458, 0.22688, 0.18459, 0.088395, 0.059321, 0.057765, 0.040668, 0.0099978, 0.0099978, 0.00999784, 0.061199, 0.077382, 0.043838, 0.38715, 0.25856, 0.23561, 0.11888, 0.056412, 0.056409, 0.035993, 0.0099978, 0.0099978, 0.00999785, 0.059543, 0.076267, 0.040438, 0.41801, 0.31225, 0.28473, 0.15017, 0.05461, 0.055364, 0.032676, 0.0099961, 0.0099961, 0.00999616, 0.058327, 0.07549, 0.038161, 0.46336, 0.31775, 0.31134, 0.16864, 0.053299, 0.0548, 0.030894, 0.0099938, 0.0099938, 0.00999387, 0.057373, 0.075205, 0.036591, 0.49252, 0.33666, 0.34447, 0.19211, 0.052136, 0.053898, 0.028695, 0.0099911, 0.0099911, 0.00999118, 0.056636, 0.074615, 0.035219, 0.52364, 0.34815, 0.36206, 0.20422, 0.051213, 0.053404, 0.027527, 0.0099879, 0.0099879, 0.00998799, 0.05602, 0.073934, 0.034359, 0.50992, 0.38009, 0.38225, 0.21813, 0.05043, 0.052847, 0.026286, 0.0099842, 0.0099842, 0.009984210, 0.055668, 0.073962, 0.033563, 0.51525, 0.38478, 0.3973, 0.22897, 0.049851, 0.052506, 0.025292, 0.00998, 0.00998, 0.0099811, 0.055276, 0.073709, 0.032925, 0.54625, 0.38502, 0.40782, 0.23643, 0.049367, 0.05212, 0.02475, 0.0099753, 0.0099753, 0.009975312, 0.054877, 0.07297, 0.032321, 0.55703, 0.39541, 0.41659, 0.24355, 0.048975, 0.051898, 0.024132, 0.0099702, 0.0099702, 0.009970213, 0.054596, 0.073085, 0.031765, 0.54956, 0.40851, 0.42707, 0.25115, 0.048565, 0.051643, 0.023694, 0.0099645, 0.0099645, 0.009964514, 0.05431, 0.073009, 0.03143, 0.56696, 0.41039, 0.4336, 0.2563, 0.048272, 0.051451, 0.023302, 0.0099584, 0.0099584, 0.009958415, 0.05406, 0.072771, 0.030999, 0.56164, 0.41919, 0.43919, 0.26051, 0.048036, 0.051295, 0.023009, 0.0099517, 0.0099517, 0.009951716, 0.053834, 0.072276, 0.030678, 0.57196, 0.42093, 0.44328, 0.26414, 0.04783, 0.051172, 0.022729, 0.0099446, 0.0099446, 0.009944617, 0.053577, 0.072335, 0.030367, 0.58215, 0.419, 0.44688, 0.2668, 0.047664, 0.051044, 0.022535, 0.009937, 0.009937, 0.00993718, 0.053448, 0.072006, 0.030096, 0.575, 0.42522, 0.44958, 0.26966, 0.047528, 0.050944, 0.022371, 0.0099289, 0.0099289, 0.009928919, 0.053259, 0.071957, 0.029836, 0.5959, 0.41816, 0.45258, 0.27204, 0.047402, 0.050845, 0.022239, 0.0099203, 0.0099203, 0.009920320, 0.053077, 0.071917, 0.029526, 0.60879, 0.41392, 0.45441, 0.27368, 0.047301, 0.050761, 0.02214, 0.0099112, 0.0099112, 0.009911221, 0.052961, 0.071599, 0.029387, 0.56503, 0.43677, 0.45631, 0.27456, 0.04722, 0.05069, 0.022046, 0.0099017, 0.0099017, 0.009901722, 0.052864, 0.071617, 0.029193, 0.58445, 0.42799, 0.45746, 0.2759, 0.047137, 0.050628, 0.021959, 0.0098916, 0.0098916, 0.009891623, 0.052629, 0.071329, 0.028944, 0.59464, 0.42518, 0.45866, 0.277, 0.047065, 0.050569, 0.021889, 0.0098811, 0.0098811, 0.009881124, 0.052497, 0.071371, 0.028764, 0.57254, 0.43689, 0.45942, 0.278, 0.047001, 0.050518, 0.021832, 0.0098701, 0.0098701, 0.009870125, 0.052393, 0.071428, 0.028541, 0.56765, 0.44037, 0.46032, 0.2787, 0.046941, 0.050474, 0.021784, 0.0098586, 0.0098586, 0.009858626, 0.052246, 0.071214, 0.028439, 0.5729, 0.43879, 0.46132, 0.27977, 0.046887, 0.050435, 0.02172, 0.0098467, 0.0098467, 0.009846727, 0.052175, 0.07089, 0.028303, 0.59449, 0.42917, 0.46239, 0.28053, 0.046835, 0.050403, 0.021667, 0.0098342, 0.0098342, 0.009834228, 0.052075, 0.070701, 0.028244, 0.60295, 0.42625, 0.46385, 0.28191, 0.046789, 0.050377, 0.021617, 0.0098213, 0.0098213, 0.009821329, 0.051997, 0.071067, 0.028108, 0.58521, 0.43614, 0.46475, 0.28292, 0.046745, 0.050361, 0.02157, 0.0098079, 0.0098079, 0.009807930, 0.051922, 0.071, 0.027901, 0.61236, 0.42383, 0.46511, 0.28362, 0.046701, 0.050348, 0.021524, 0.0097941, 0.0097941, 0.009794131, 0.051872, 0.070737, 0.027774, 0.62071, 0.42165, 0.46607, 0.28453, 0.046654, 0.05034, 0.021475, 0.0097798, 0.0097798, 0.009779832, 0.05168, 0.070582, 0.027777, 0.58882, 0.43789, 0.46703, 0.28551, 0.046607, 0.050339, 0.02142, 0.009765, 0.009765, 0.00976533, 0.051612, 0.070762, 0.027565, 0.59094, 0.43816, 0.46795, 0.28637, 0.046563, 0.050342, 0.02138, 0.0097497, 0.0097497, 0.009749734, 0.051544, 0.070445, 0.027422, 0.58, 0.44565, 0.46896, 0.28706, 0.046517, 0.050339, 0.021334, 0.009734, 0.009734, 0.00973435, 0.051499, 0.070687, 0.027361, 0.60279, 0.43692, 0.46993, 0.28788, 0.046476, 0.050338, 0.02129, 0.0097178, 0.0097178, 0.009717836, 0.051421, 0.070385, 0.027363, 0.57595, 0.45075, 0.47137, 0.28923, 0.046433, 0.050337, 0.021241, 0.0097011, 0.0097011, 0.009701137, 0.051288, 0.070366, 0.027199, 0.57659, 0.4512, 0.47223, 0.28996, 0.046385, 0.050332, 0.021189, 0.009684, 0.009684, 0.00968438, 0.051249, 0.070556, 0.027186, 0.58312, 0.44984, 0.47376, 0.29134, 0.046333, 0.050325, 0.021127, 0.0096664, 0.0096664, 0.009666439, 0.051224, 0.070369, 0.026999, 0.56718, 0.46055, 0.47543, 0.29283, 0.046283, 0.050315, 0.02107, 0.0096484, 0.0096484, 0.009648440, 0.051065, 0.07025, 0.026815, 0.57623, 0.45931, 0.47704, 0.29384, 0.04624, 0.050295, 0.021016, 0.0096299, 0.0096299, 0.0096299repvgg-v5s2-not-fuse
这个部分是只将卷积大小为3x3，并且stride=1的进行替换，对于其他大小的卷积或者下采样的Conv没有替换。
- Model Summary: 529 layers, 18323901 parameters, 18323901 gradients, 22.1 GFLOPs bs=256
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.092004, 0.084015, 0.082964, 0.029228, 0.040308, 0.0036937, 0.0011781, 0.08216, 0.064867, 0.077113, 0.0033297, 0.0033297, 0.0700321, 0.074385, 0.083075, 0.071424, 0.36081, 0.083457, 0.032672, 0.010698, 0.070906, 0.063654, 0.062175, 0.0066629, 0.0066629, 0.0400322, 0.068213, 0.080396, 0.058372, 0.33255, 0.16075, 0.099448, 0.042357, 0.064713, 0.060401, 0.051163, 0.0099954, 0.0099954, 0.0100313, 0.063901, 0.079088, 0.048938, 0.37051, 0.21196, 0.17536, 0.082413, 0.059104, 0.057996, 0.04138, 0.0099978, 0.0099978, 0.00999784, 0.06108, 0.077382, 0.043561, 0.40702, 0.26596, 0.23681, 0.12011, 0.056339, 0.056411, 0.035908, 0.0099978, 0.0099978, 0.00999785, 0.059405, 0.076233, 0.040388, 0.41548, 0.3136, 0.28489, 0.15177, 0.054522, 0.055313, 0.032694, 0.0099961, 0.0099961, 0.00999616, 0.058182, 0.075428, 0.038158, 0.4367, 0.33047, 0.3094, 0.1678, 0.05316, 0.054771, 0.030924, 0.0099938, 0.0099938, 0.00999387, 0.05735, 0.075031, 0.036529, 0.51722, 0.33262, 0.3434, 0.18961, 0.052, 0.053987, 0.028712, 0.0099911, 0.0099911, 0.00999118, 0.056641, 0.07458, 0.035336, 0.53165, 0.34665, 0.36402, 0.20534, 0.051109, 0.05336, 0.027521, 0.0099879, 0.0099879, 0.00998799, 0.05608, 0.074169, 0.034309, 0.51649, 0.3766, 0.38203, 0.21764, 0.050346, 0.052864, 0.026372, 0.0099842, 0.0099842, 0.009984210, 0.055596, 0.073769, 0.033533, 0.52767, 0.39115, 0.39924, 0.22918, 0.049731, 0.052498, 0.02528, 0.00998, 0.00998, 0.0099811, 0.055214, 0.073445, 0.032875, 0.51122, 0.40731, 0.40802, 0.23782, 0.049252, 0.052116, 0.024745, 0.0099753, 0.0099753, 0.009975312, 0.054828, 0.073024, 0.032236, 0.545, 0.3989, 0.41737, 0.24483, 0.048801, 0.051849, 0.024273, 0.0099702, 0.0099702, 0.009970213, 0.054548, 0.073003, 0.031797, 0.53235, 0.41676, 0.42498, 0.25128, 0.048508, 0.051585, 0.023823, 0.0099645, 0.0099645, 0.009964514, 0.054265, 0.072806, 0.031344, 0.54557, 0.41953, 0.43157, 0.25618, 0.048195, 0.051413, 0.023407, 0.0099584, 0.0099584, 0.009958415, 0.054041, 0.07252, 0.030969, 0.56267, 0.4155, 0.43702, 0.26028, 0.047964, 0.051246, 0.023063, 0.0099517, 0.0099517, 0.0099517
repvgg-v5s2-fuse
在上次 repvgg-v5s2修改中，只修改了kernel_size=3，stride=1的卷积，对于其他大小的卷积或者stride!=1的卷积，没有进行fuse_conv_bn操作，而在这次实验中则完善了这一操作！这个BottleNeck里面的cv1,cv2的kernel_size都是3，但是yolov5s里面的是k1=1与k2=3，所以这个需要变一下
#experimental.py
# detect.py加载pt文件时，调用了yolo.py中的fuse操作（下面），对conv与bn进行了fusedef attempt_load(weights, map_location=None, inplace=True, fuse=True):from models.yolo import Detect, Model# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=amodel = Ensemble()for w in weights if isinstance(weights, list) else [weights]:ckpt = torch.load(attempt_download(w), map_location=map_location) # loadif fuse:model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 modelelse:model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse#yolo.pydef fuse(self): # fuse model Conv2d() + BatchNorm2d() layersLOGGER.info('Fusing layers... ')for m in self.model.modules():if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):m.brb_rep = fuse_conv_and_bn(m.brb_rep, m.bn) # update convdelattr(m, 'bn') # remove batchnormm.forward = m.forward_fuse # update forwardself.info()return self#torch_utils.py
#这个可以根据自己的需要进行修改，比如conv.in_channels修改成conv.conv.in_channels，具体要看自己的网络模块是什么样的。def fuse_conv_and_bn(conv, bn):# Fuse convolution and batchnorm layers = nn.Conv2d(conv.in_channels,conv.out_channels,kernel_size=conv.kernel_size,stride=conv.stride,padding=conv.padding,groups=conv.groups,bias=True).requires_grad_(False).to(conv.weight.device)# prepare filtersw_conv = conv.weight.clone().view(conv.out_channels, -1)w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))# prepare spatial biasb_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.biasb_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)return fusedconvclass Conv(nn.Module):def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True,deploy=False): # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()......def forward(self, inputs):#如果不进行_switch_to_deploy,下面两个return就足够了if (self.deploy):#Conv._switch_to_deploy 会改变deployif hasattr(self,'bn'):#说明不是结构重参数化return self.act(self.bn(self.brb_rep(inputs)))else:#说明是结构重参数化，没有bn操作return self.act(self.brb_rep(inputs))#下面是为了不进行_switch_to_deploy，进行的forward操作if (self.brb_identity == None):identity_out = 0else:identity_out = self.brb_identity(inputs)return self.act(self.brb_1x1(inputs) + self.brb_3x3(inputs) + identity_out)def forward_fuse(self, inputs):if (self.deploy):#Conv._switch_to_deploy 会改变deploy# print(self.brb_rep(torch.randn(1,3,4,4)).shape)# print('self.brb_rep.kernel_size',self.brb_rep.kernel_size)return self.act(self.brb_rep(inputs))if (self.brb_identity == None):identity_out = 0else:identity_out = self.brb_identity(inputs)return self.act(self.brb_1x1(inputs) + self.brb_3x3(inputs) + identity_out)记录
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091684, 0.083873, 0.083003, 0.11889, 0.036322, 0.0047963, 0.0013873, 0.080382, 0.062765, 0.077113, 0.0033297, 0.0033297, 0.0700321, 0.074254, 0.08325, 0.071897, 0.28534, 0.1069, 0.038758, 0.014097, 0.069871, 0.060908, 0.063017, 0.0066629, 0.0066629, 0.0400322, 0.068055, 0.080653, 0.058831, 0.30226, 0.15678, 0.097203, 0.041169, 0.064179, 0.060451, 0.049972, 0.0099954, 0.0099954, 0.0100313, 0.063864, 0.078561, 0.049357, 0.37208, 0.20302, 0.17114, 0.081694, 0.059796, 0.057443, 0.041513, 0.0099978, 0.0099978, 0.00999784, 0.061138, 0.077359, 0.04392, 0.34799, 0.27356, 0.23034, 0.11579, 0.056402, 0.056551, 0.036675, 0.0099978, 0.0099978, 0.00999785, 0.059469, 0.076227, 0.040648, 0.45373, 0.2866, 0.27911, 0.14818, 0.054577, 0.055503, 0.033043, 0.0099961, 0.0099961, 0.00999616, 0.058296, 0.075598, 0.038351, 0.45885, 0.32383, 0.31248, 0.17017, 0.053094, 0.054646, 0.030785, 0.0099938, 0.0099938, 0.00999387, 0.057482, 0.075143, 0.036739, 0.48984, 0.34399, 0.34029, 0.18835, 0.052042, 0.053939, 0.028925, 0.0099911, 0.0099911, 0.00999118, 0.056705, 0.074649, 0.035441, 0.4915, 0.3654, 0.36197, 0.20541, 0.051114, 0.05332, 0.027579, 0.0099879, 0.0099879, 0.00998799, 0.056144, 0.074082, 0.034403, 0.52628, 0.37434, 0.38172, 0.21816, 0.050361, 0.052842, 0.026346, 0.0099842, 0.0099842, 0.009984210, 0.055666, 0.073836, 0.033553, 0.51832, 0.39206, 0.39602, 0.2287, 0.049761, 0.052426, 0.025471, 0.00998, 0.00998, 0.0099811, 0.055231, 0.073437, 0.032997, 0.56388, 0.37978, 0.40593, 0.23718, 0.04923, 0.0521, 0.024748, 0.0099753, 0.0099753, 0.009975312, 0.054889, 0.073394, 0.032308, 0.57626, 0.38826, 0.41714, 0.24481, 0.048803, 0.051869, 0.024245, 0.0099702, 0.0099702, 0.009970213, 0.054554, 0.072821, 0.031894, 0.53876, 0.41098, 0.42532, 0.25248, 0.048478, 0.051596, 0.023739, 0.0099645, 0.0099645, 0.009964514, 0.054293, 0.072961, 0.031428, 0.55517, 0.41513, 0.43209, 0.25723, 0.048174, 0.051402, 0.02335, 0.0099584, 0.0099584, 0.009958415, 0.054051, 0.072379, 0.03106, 0.5711, 0.4148, 0.43923, 0.26223, 0.047915, 0.051225, 0.023025, 0.0099517, 0.0099517, 0.009951716, 0.053788, 0.072349, 0.030638, 0.56422, 0.42605, 0.44355, 0.26615, 0.047721, 0.051081, 0.022759, 0.0099446, 0.0099446, 0.009944617, 0.053498, 0.072054, 0.030305, 0.58218, 0.4219, 0.44725, 0.26785, 0.047562, 0.050984, 0.022552, 0.009937, 0.009937, 0.00993718, 0.053371, 0.071944, 0.030003, 0.57388, 0.42997, 0.45016, 0.27031, 0.04744, 0.050874, 0.022405, 0.0099289, 0.0099289, 0.009928919, 0.053229, 0.071684, 0.029826, 0.58115, 0.42835, 0.4526, 0.27211, 0.047332, 0.050791, 0.02227, 0.0099203, 0.0099203, 0.009920320, 0.053079, 0.071805, 0.029529, 0.58502, 0.42681, 0.45375, 0.27357, 0.047245, 0.050707, 0.022169, 0.0099112, 0.0099112, 0.009911221, 0.052902, 0.071675, 0.029337, 0.58125, 0.43068, 0.45569, 0.27526, 0.047169, 0.050641, 0.022076, 0.0099017, 0.0099017, 0.009901722, 0.052779, 0.071798, 0.029114, 0.57623, 0.43864, 0.45683, 0.27636, 0.047096, 0.050588, 0.021995, 0.0098916, 0.0098916, 0.009891623, 0.052688, 0.07148, 0.028993, 0.5592, 0.44757, 0.45836, 0.27782, 0.047032, 0.050533, 0.021918, 0.0098811, 0.0098811, 0.009881124, 0.052492, 0.071561, 0.028844, 0.56807, 0.4431, 0.45924, 0.27891, 0.046975, 0.050497, 0.021851, 0.0098701, 0.0098701, 0.009870125, 0.052359, 0.071399, 0.028671, 0.5645, 0.44534, 0.46058, 0.28024, 0.046923, 0.05046, 0.021784, 0.0098586, 0.0098586, 0.009858626, 0.052267, 0.071078, 0.028537, 0.56427, 0.44617, 0.46203, 0.28136, 0.046877, 0.05043, 0.021728, 0.0098467, 0.0098467, 0.009846727, 0.052167, 0.071205, 0.02834, 0.56993, 0.44432, 0.463, 0.28212, 0.046831, 0.050411, 0.021681, 0.0098342, 0.0098342, 0.009834228, 0.052017, 0.070836, 0.028275, 0.58049, 0.44164, 0.46401, 0.28307, 0.046781, 0.050408, 0.021631, 0.0098213, 0.0098213, 0.009821329, 0.051979, 0.070958, 0.028054, 0.57612, 0.44456, 0.46563, 0.28385, 0.046736, 0.050405, 0.021588, 0.0098079, 0.0098079, 0.009807930, 0.051819, 0.070996, 0.028032, 0.58498, 0.4407, 0.46687, 0.28457, 0.046697, 0.050407, 0.021544, 0.0097941, 0.0097941, 0.009794131, 0.051814, 0.070874, 0.027923, 0.59036, 0.43892, 0.46802, 0.28548, 0.046656, 0.050411, 0.021493, 0.0097798, 0.0097798, 0.009779832, 0.051686, 0.070831, 0.027725, 0.57675, 0.44805, 0.46862, 0.28645, 0.046615, 0.050426, 0.021445, 0.009765, 0.009765, 0.00976533, 0.051667, 0.070634, 0.0277, 0.58037, 0.44705, 0.46963, 0.28724, 0.046574, 0.050447, 0.021389, 0.0097497, 0.0097497, 0.009749734, 0.051521, 0.070458, 0.027497, 0.58719, 0.44374, 0.47044, 0.28792, 0.046535, 0.050461, 0.021332, 0.009734, 0.009734, 0.00973435, 0.051506, 0.070662, 0.027441, 0.5885, 0.44439, 0.47186, 0.28942, 0.046491, 0.050471, 0.021269, 0.0097178, 0.0097178, 0.009717836, 0.051418, 0.07035, 0.027413, 0.59529, 0.44237, 0.47313, 0.29034, 0.04645, 0.050473, 0.021213, 0.0097011, 0.0097011, 0.009701137, 0.051342, 0.07035, 0.027347, 0.59307, 0.4456, 0.47436, 0.2915, 0.046409, 0.050474, 0.021146, 0.009684, 0.009684, 0.00968438, 0.051249, 0.070294, 0.027215, 0.60617, 0.44072, 0.47558, 0.29213, 0.046363, 0.050472, 0.02108, 0.0096664, 0.0096664, 0.0096664bs128-v5m-only3-repvgg-失败
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091429, 0.083921, 0.083333, 0.18123, 0.037406, 0.0054993, 0.0017863, 0.080575, 0.065993, 0.077819, 0.0033297, 0.0033297, 0.0700321, 0.07315, 0.083372, 0.072354, 0.34694, 0.080206, 0.043477, 0.016405, 0.068264, 0.064052, 0.060966, 0.0066629, 0.0066629, 0.0400322, 0.066702, 0.081285, 0.059143, 0.35, 0.15084, 0.10672, 0.04587, 0.062273, 0.061312, 0.048442, 0.0099954, 0.0099954, 0.0100313, 0.062169, 0.079481, 0.049128, 0.30166, 0.24924, 0.1853, 0.091003, 0.057319, 0.059455, 0.039904, 0.0099978, 0.0099978, 0.00999784, 0.059529, 0.076886, 0.043603, 0.40681, 0.28908, 0.25994, 0.13575, 0.054467, 0.057602, 0.035494, 0.0099978, 0.0099978, 0.00999785, 0.057298, 0.076357, 0.040525, 0.48307, 0.30996, 0.30937, 0.16804, 0.052252, 0.056752, 0.031491, 0.0099961, 0.0099961, 0.00999616, 0.056366, 0.075171, 0.038025, 0.47274, 0.35327, 0.34361, 0.19272, 0.050701, 0.055754, 0.02923, 0.0099938, 0.0099938, 0.00999387, 0.055512, 0.075627, 0.036506, 0.51957, 0.36741, 0.37435, 0.21306, 0.049501, 0.054915, 0.027218, 0.0099911, 0.0099911, 0.00999118, 0.054382, 0.072708, 0.034843, 0.54941, 0.37464, 0.39539, 0.22905, 0.048637, 0.054252, 0.025949, 0.0099879, 0.0099879, 0.00998799, 0.054007, 0.073536, 0.033998, 0.53968, 0.39345, 0.41214, 0.24421, 0.047677, 0.05369, 0.024703, 0.0099842, 0.0099842, 0.009984210, 0.053224, 0.072996, 0.03322, 0.53395, 0.41462, 0.4247, 0.2539, 0.047103, 0.053297, 0.023964, 0.00998, 0.00998, 0.0099811, 0.053371, 0.07458, 0.032661, 0.54502, 0.42801, 0.43888, 0.26576, 0.046506, 0.052943, 0.023176, 0.0099753, 0.0099753, 0.009975312, 0.052681, 0.072668, 0.032079, 0.57409, 0.42684, 0.44958, 0.27447, 0.046053, 0.052622, 0.022701, 0.0099702, 0.0099702, 0.009970213, 0.052236, 0.071905, 0.031348, 0.56773, 0.43854, 0.45804, 0.28024, 0.045605, 0.052407, 0.022196, 0.0099645, 0.0099645, 0.009964514, 0.052264, 0.072108, 0.030936, 0.57746, 0.4412, 0.46565, 0.28636, 0.045278, 0.052168, 0.02181, 0.0099584, 0.0099584, 0.009958415, 0.051566, 0.071671, 0.0305, 0.57607, 0.45039, 0.47213, 0.29103, 0.045031, 0.05199, 0.02146, 0.0099517, 0.0099517, 0.009951716, 0.051606, 0.071175, 0.030377, 0.58814, 0.44854, 0.47788, 0.29613, 0.044854, 0.051837, 0.021206, 0.0099446, 0.0099446, 0.009944617, 0.051317, 0.071775, 0.029695, 0.59891, 0.44795, 0.48104, 0.29932, 0.044679, 0.051704, 0.021012, 0.009937, 0.009937, 0.009937v5m
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.088815, 0.083758, 0.083028, 0.10967, 0.060776, 0.0084766, 0.0028998, 0.07658, 0.065096, 0.075192, 0.0033297, 0.0033297, 0.0700321, 0.07153, 0.082675, 0.069671, 0.25921, 0.12556, 0.050926, 0.021388, 0.067456, 0.063615, 0.058973, 0.0066629, 0.0066629, 0.0400322, 0.065678, 0.079942, 0.056423, 0.28735, 0.19372, 0.13283, 0.059436, 0.060568, 0.061682, 0.046271, 0.0099954, 0.0099954, 0.0100313, 0.061338, 0.078504, 0.04723, 0.40063, 0.24958, 0.22877, 0.11608, 0.055906, 0.058371, 0.037091, 0.0099978, 0.0099978, 0.00999784, 0.058455, 0.076397, 0.042069, 0.42689, 0.30947, 0.28819, 0.15312, 0.053267, 0.057035, 0.032819, 0.0099978, 0.0099978, 0.00999785, 0.056665, 0.075084, 0.038737, 0.48373, 0.33178, 0.32958, 0.18127, 0.051449, 0.05598, 0.03003, 0.0099961, 0.0099961, 0.00999616, 0.055591, 0.075298, 0.036506, 0.49513, 0.36799, 0.36491, 0.2067, 0.049934, 0.054871, 0.027925, 0.0099938, 0.0099938, 0.00999387, 0.054502, 0.073199, 0.035426, 0.49643, 0.39808, 0.38942, 0.22583, 0.048767, 0.054358, 0.026238, 0.0099911, 0.0099911, 0.00999118, 0.053876, 0.07238, 0.033299, 0.52135, 0.41381, 0.41562, 0.24501, 0.04781, 0.053518, 0.024843, 0.0099879, 0.0099879, 0.00998799, 0.053236, 0.072389, 0.032481, 0.54612, 0.41269, 0.43074, 0.25796, 0.046989, 0.053086, 0.023894, 0.0099842, 0.0099842, 0.009984210, 0.052832, 0.07241, 0.031733, 0.55228, 0.43234, 0.44671, 0.26953, 0.046382, 0.052618, 0.023047, 0.00998, 0.00998, 0.0099811, 0.05282, 0.074112, 0.031097, 0.5533, 0.44669, 0.46024, 0.28065, 0.045857, 0.052293, 0.022248, 0.0099753, 0.0099753, 0.009975312, 0.052155, 0.071912, 0.030904, 0.55584, 0.45631, 0.46781, 0.28701, 0.045427, 0.051986, 0.021778, 0.0099702, 0.0099702, 0.009970213, 0.051747, 0.072218, 0.03014, 0.60159, 0.44424, 0.47684, 0.29523, 0.045071, 0.051834, 0.021251, 0.0099645, 0.0099645, 0.009964514, 0.051199, 0.071139, 0.029753, 0.60888, 0.44745, 0.4834, 0.30013, 0.044758, 0.051615, 0.020943, 0.0099584, 0.0099584, 0.009958415, 0.051116, 0.071044, 0.029682, 0.61669, 0.45095, 0.48788, 0.30411, 0.044482, 0.051461, 0.020708, 0.0099517, 0.0099517, 0.009951716, 0.051165, 0.071407, 0.029131, 0.58867, 0.46949, 0.49298, 0.30788, 0.044266, 0.051328, 0.020452, 0.0099446, 0.0099446, 0.009944617, 0.050888, 0.070982, 0.029018, 0.60247, 0.46829, 0.49698, 0.3109, 0.044097, 0.051225, 0.020252, 0.009937, 0.009937, 0.00993718, 0.050531, 0.070728, 0.028839, 0.62038, 0.46144, 0.49886, 0.31304, 0.04396, 0.051144, 0.020106, 0.0099289, 0.0099289, 0.009928919, 0.050503, 0.070549, 0.028389, 0.61684, 0.4655, 0.50101, 0.31475, 0.043849, 0.051041, 0.019979, 0.0099203, 0.0099203, 0.009920320, 0.050645, 0.070725, 0.028245, 0.60357, 0.47478, 0.5033, 0.31661, 0.043755, 0.050947, 0.019874, 0.0099112, 0.0099112, 0.009911221, 0.050031, 0.069934, 0.027804, 0.60496, 0.47581, 0.50527, 0.31852, 0.043667, 0.050866, 0.01977, 0.0099017, 0.0099017, 0.009901722, 0.049924, 0.069962, 0.027742, 0.61439, 0.47212, 0.50718, 0.32007, 0.043592, 0.050781, 0.019677, 0.0098916, 0.0098916, 0.009891623, 0.049774, 0.070259, 0.027356, 0.62347, 0.4707, 0.50993, 0.32225, 0.043526, 0.050693, 0.019594, 0.0098811, 0.0098811, 0.009881124, 0.049478, 0.06908, 0.0275, 0.59173, 0.49002, 0.51115, 0.32364, 0.043463, 0.050619, 0.019518, 0.0098701, 0.0098701, 0.009870125, 0.049521, 0.070211, 0.027425, 0.59696, 0.4898, 0.51278, 0.32486, 0.043407, 0.050557, 0.019451, 0.0098586, 0.0098586, 0.009858626, 0.049435, 0.07008, 0.027264, 0.61438, 0.48112, 0.51381, 0.32583, 0.043351, 0.050498, 0.019382, 0.0098467, 0.0098467, 0.009846727, 0.049354, 0.070545, 0.026766, 0.6064, 0.48604, 0.51499, 0.32726, 0.043301, 0.050441, 0.019321, 0.0098342, 0.0098342, 0.009834228, 0.049133, 0.069629, 0.02687, 0.63298, 0.47289, 0.51647, 0.32861, 0.043251, 0.050395, 0.019262, 0.0098213, 0.0098213, 0.009821329, 0.049244, 0.070239, 0.026565, 0.61425, 0.48472, 0.51749, 0.32981, 0.043202, 0.050362, 0.0192, 0.0098079, 0.0098079, 0.009807930, 0.049247, 0.069914, 0.026879, 0.62765, 0.47932, 0.51836, 0.33032, 0.043154, 0.050333, 0.019143, 0.0097941, 0.0097941, 0.009794131, 0.048997, 0.070243, 0.026534, 0.64588, 0.47083, 0.51933, 0.33128, 0.04311, 0.05031, 0.019085, 0.0097798, 0.0097798, 0.009779832, 0.048746, 0.06989, 0.026604, 0.62604, 0.48261, 0.52032, 0.3324, 0.043065, 0.050294, 0.019024, 0.009765, 0.009765, 0.00976533, 0.048844, 0.069789, 0.026362, 0.60369, 0.49778, 0.52143, 0.3331, 0.04302, 0.050284, 0.018967, 0.0097497, 0.0097497, 0.009749734, 0.048838, 0.06909, 0.026239, 0.6214, 0.48986, 0.52276, 0.33407, 0.042976, 0.050265, 0.018912, 0.009734, 0.009734, 0.00973435, 0.048356, 0.069284, 0.025822, 0.61233, 0.49574, 0.52344, 0.33489, 0.042929, 0.050244, 0.018861, 0.0097178, 0.0097178, 0.009717836, 0.048366, 0.068073, 0.025951, 0.61853, 0.49364, 0.5246, 0.3363, 0.042877, 0.050227, 0.018803, 0.0097011, 0.0097011, 0.009701137, 0.048487, 0.069332, 0.025808, 0.63126, 0.48776, 0.52577, 0.33738, 0.042821, 0.050208, 0.01875, 0.009684, 0.009684, 0.00968438, 0.048189, 0.069316, 0.025894, 0.64172, 0.48407, 0.52724, 0.33821, 0.042764, 0.050177, 0.018681, 0.0096664, 0.0096664, 0.009666439, 0.048339, 0.069556, 0.025417, 0.65185, 0.48204, 0.52911, 0.33938, 0.042711, 0.050147, 0.018621, 0.0096484, 0.0096484, 0.009648440, 0.048066, 0.068588, 0.025469, 0.65607, 0.4814, 0.53022, 0.34057, 0.042654, 0.050112, 0.018563, 0.0096299, 0.0096299, 0.009629941, 0.048106, 0.069846, 0.025577, 0.66145, 0.48068, 0.53182, 0.34192, 0.042593, 0.050081, 0.018496, 0.009611, 0.009611, 0.00961142, 0.04817, 0.069178, 0.025513, 0.65954, 0.48355, 0.53306, 0.34304, 0.042531, 0.050039, 0.018439, 0.0095916, 0.0095916, 0.009591643, 0.047838, 0.068633, 0.0254, 0.63862, 0.49544, 0.5345, 0.34428, 0.042466, 0.049994, 0.01837, 0.0095717, 0.0095717, 0.009571744, 0.047805, 0.068237, 0.025414, 0.62767, 0.50361, 0.53601, 0.34541, 0.0424, 0.049956, 0.018304, 0.0095514, 0.0095514, 0.009551445, 0.047678, 0.068207, 0.025349, 0.63458, 0.5013, 0.53745, 0.34681, 0.042332, 0.04991, 0.018235, 0.0095307, 0.0095307, 0.009530746, 0.047662, 0.068054, 0.02531, 0.62932, 0.50579, 0.53886, 0.34872, 0.042266, 0.049872, 0.018165, 0.0095095, 0.0095095, 0.009509547, 0.048001, 0.069291, 0.024793, 0.63726, 0.50436, 0.54061, 0.35014, 0.0422, 0.049825, 0.0181, 0.0094879, 0.0094879, 0.009487948, 0.047774, 0.068069, 0.025169, 0.65425, 0.4975, 0.54264, 0.3516, 0.042133, 0.049773, 0.018031, 0.0094659, 0.0094659, 0.009465949, 0.047674, 0.06858, 0.024896, 0.64272, 0.50575, 0.54415, 0.35326, 0.04207, 0.049724, 0.017963, 0.0094434, 0.0094434, 0.009443450, 0.047703, 0.068074, 0.025099, 0.64057, 0.50878, 0.54578, 0.35462, 0.042001, 0.04967, 0.017888, 0.0094205, 0.0094205, 0.009420551, 0.047624, 0.068676, 0.025119, 0.65135, 0.50244, 0.54779, 0.35586, 0.041937, 0.049611, 0.017818, 0.0093971, 0.0093971, 0.009397152, 0.047448, 0.068091, 0.024714, 0.62977, 0.51681, 0.54856, 0.35716, 0.041875, 0.049551, 0.017752, 0.0093733, 0.0093733, 0.009373353, 0.047625, 0.068465, 0.024857, 0.64038, 0.51344, 0.55018, 0.3584, 0.041815, 0.049497, 0.017687, 0.0093491, 0.0093491, 0.009349154, 0.047647, 0.067882, 0.02476, 0.64501, 0.51334, 0.55105, 0.35947, 0.041749, 0.049447, 0.017623, 0.0093245, 0.0093245, 0.009324555, 0.047092, 0.068041, 0.024622, 0.64294, 0.51586, 0.55202, 0.36056, 0.041692, 0.049388, 0.017557, 0.0092995, 0.0092995, 0.009299556, 0.047486, 0.069097, 0.024603, 0.64305, 0.51754, 0.55371, 0.36145, 0.041635, 0.049338, 0.017489, 0.009274, 0.009274, 0.00927457, 0.047281, 0.067822, 0.024274, 0.65651, 0.51091, 0.55552, 0.36304, 0.041588, 0.049283, 0.017427, 0.0092481, 0.0092481, 0.009248158, 0.047146, 0.068453, 0.024524, 0.64516, 0.51803, 0.55695, 0.3643, 0.041535, 0.04923, 0.017362, 0.0092219, 0.0092219, 0.0092219
【日志
repvgg-v5s
- bs512
- bs128
- v5s-lite-g
- v5s-g
- v5m-bs128
- yolov5s
- repvgg-v5s1
- repvgg-v5s2-not-fuse
- repvgg-v5s2-fuse
- bs128-v5m-only3-repvgg-失败
- v5m
v5lite-g-sppf-conv6-chunk 36.7map
v5lite-g-sppf-conv6 0.3645
v5-g2 0.3559
yolov5s-LC3-chunk2-rep-512 0.36192
LCB4 0.35085,
chunk2 0.35664
shuffle 0.35926
bs512
| map50-95 | v5s(0.357) | v5s-C3-shuffle | v5s-LC3chunk2 | v5s-LCB5( 0.3661) |
|---|---|---|---|---|
| epoch10 | 0.14827 | 0.14992 | 0.15495 | |
| epoch20 | 0.21336 | 0.21745 | 0.22601 | |
| epoch30 | 0.25346 | 0.25368 | 0.26217 | |
| epoch50 | 0.28785 | 0.288 | 0.29716 | |
| epoch80 | 0.30128 | ---- | 0.31201 | 0.3137 |
| epoch150 | 0.30411 | ---- | 0.31427 | 0.314 |
| map50-95 | v5s | v5s-LCBlock3 | v5s-LCBlock4 |
|---|---|---|---|
| epoch10 | 0.14827 | 0.15102 | 0.14323 |
| epoch20 | 0.21336 | 0.22605 | 0.21101 |
| epoch30 | 0.25346 | 0.26368 | 0.25108 |
| epoch50 | 0.28785 | 0.29997 | 0.28791 |
| epoch80 | 0.30128 | 0.31368 | 0.30026 |
| epoch150 | 0.30411 | 0.31626 | 0.29736 |
| epoch200 | xxxxxxxxxxx | 0.32948 | 0.31126 |
map50-95 | v5s | v5s-LCB3-chunk2|chunk2
-------- | ----- |
epoch10 | 0.14827 | 0.15495 |
epoch20 | 0.21336 | 0.22601
epoch30 | 0.25346 | 0.26217
epoch50 | 0.28785 | 0.29716
epoch80 | 0.30128 | 0.31201
epoch150 | 0.30411 | 0.31427 | 0.301
epoch200|xxxxxxxxxxx |
bs128
| map50-95 | v5s | v5s-g | v5s-lite-g |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21338 | 0.21598 |
| epoch20 | 0.24671 | 0.25771 | 0.25994 |
| epoch30 | 0.2564 | 0.26827 | 0.27078 |
| epoch50 | 0.27553 | 0.28786 | 0.29147 |
| epoch80 | 0.30649 | 0.31702 | 0.32149 |
| epoch150 | 0.34457 0.34509(sppf) |
| map50-95 | v5s | v5s-g-sppf | v5s-repconv |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21671 | 0.20472 |
| epoch20 | 0.24671 | 0.26189 | 0.24597 |
| epoch30 | 0.2564 | 0.27277 | 0.25588 |
| epoch50 | 0.27553 | 0.2919 | 0.27353 |
| epoch80 | 0.30649 | 0.31806 | 0.30048 |
| map50-95 | v5s | v5g-Focus->6 | v5s-lite-g |
|---|---|---|---|
| epoch10 | 0.20609 | 0.21466 | 0.17235 |
| epoch20 | 0.24671 | 0.25808 | 0.21379 |
| epoch30 | 0.2564 | 0.26928 | 0.21894 |
| epoch50 | 0.27553 | 0.28971 | 0.23417 |
| epoch80 | 0.30649 | 0.31824 | ---- |
| map50-95 | v5s | v5-LCBlock | v5s-LCBlock2 | v5s-LCB3-chunk2 |
|---|---|---|---|---|
| epoch10 | 0.20609 | 0.20474 | 0.20699 | 0.21211 |
| epoch20 | 0.24671 | 0.24611 | 0.24932 | 0.25482 |
| epoch30 | 0.2564 | 0.25629 | 0.2584 | 0.26505 |
| epoch50 | 0.27553 | 0.27647 | 0.27931 | 0.28598, |
| epoch80 | 0.30649 | 0.30654 | 0.30737 | |
| epoch150 | 0.33338 | 0.33523 | ---- |
v5s-lite-g
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091764, 0.083605, 0.083667, 0.016046, 0.056931, 0.0041556, 0.0014131, 0.07822, 0.0657, 0.0787, 0.0033297, 0.0033297, 0.0700321, 0.073686, 0.083846, 0.075502, 0.32996, 0.057096, 0.020941, 0.0088895, 0.069293, 0.063517, 0.068443, 0.0066629, 0.0066629, 0.0400322, 0.06799, 0.082278, 0.063985, 0.3211, 0.13373, 0.072504, 0.03121, 0.063444, 0.062623, 0.054571, 0.0099954, 0.0099954, 0.0100313, 0.063771, 0.080539, 0.053533, 0.33994, 0.21106, 0.14836, 0.070977, 0.058648, 0.060657, 0.044437, 0.0099978, 0.0099978, 0.00999784, 0.061017, 0.077907, 0.048235, 0.34807, 0.244, 0.19708, 0.10435, 0.056184, 0.058426, 0.039842, 0.0099978, 0.0099978, 0.00999785, 0.059494, 0.077005, 0.044746, 0.39311, 0.27296, 0.24094, 0.13153, 0.054184, 0.05767, 0.036521, 0.0099961, 0.0099961, 0.00999616, 0.058339, 0.076628, 0.042245, 0.42702, 0.29912, 0.278, 0.15608, 0.052886, 0.056604, 0.034162, 0.0099938, 0.0099938, 0.00999387, 0.05765, 0.076134, 0.040563, 0.43897, 0.32715, 0.30912, 0.17754, 0.051631, 0.055954, 0.032001, 0.0099911, 0.0099911, 0.00999118, 0.056604, 0.07506, 0.039379, 0.46377, 0.34762, 0.33084, 0.19195, 0.050889, 0.055378, 0.030631, 0.0099879, 0.0099879, 0.00998799, 0.056356, 0.075845, 0.03818, 0.51648, 0.34182, 0.34928, 0.2052, 0.050079, 0.054832, 0.029381, 0.0099842, 0.0099842, 0.009984210, 0.055955, 0.075936, 0.037408, 0.49384, 0.37324, 0.36567, 0.21598, 0.049515, 0.054541, 0.028365, 0.00998, 0.00998, 0.0099811, 0.055443, 0.07421, 0.036685, 0.50411, 0.38051, 0.37694, 0.22426, 0.049056, 0.054128, 0.027581, 0.0099753, 0.0099753, 0.009975312, 0.055402, 0.074553, 0.035941, 0.53431, 0.37807, 0.38842, 0.23277, 0.048627, 0.053846, 0.027027, 0.0099702, 0.0099702, 0.009970213, 0.054766, 0.074203, 0.035406, 0.52697, 0.39371, 0.39624, 0.2389, 0.048288, 0.05367, 0.026508, 0.0099645, 0.0099645, 0.009964514, 0.054625, 0.073913, 0.034909, 0.55272, 0.38366, 0.40125, 0.24303, 0.048002, 0.053492, 0.026123, 0.0099584, 0.0099584, 0.009958415, 0.054317, 0.073803, 0.034637, 0.54119, 0.39693, 0.40664, 0.24744, 0.047798, 0.053324, 0.025797, 0.0099517, 0.0099517, 0.009951716, 0.054204, 0.073825, 0.034358, 0.54239, 0.40287, 0.41212, 0.25056, 0.047653, 0.053182, 0.025574, 0.0099446, 0.0099446, 0.009944617, 0.053936, 0.072659, 0.03417, 0.53188, 0.41191, 0.416, 0.25368, 0.047536, 0.053067, 0.025362, 0.009937, 0.009937, 0.00993718, 0.0539, 0.073468, 0.033832, 0.57219, 0.3927, 0.41887, 0.25594, 0.047424, 0.052961, 0.025217, 0.0099289, 0.0099289, 0.009928919, 0.053708, 0.072787, 0.033427, 0.57544, 0.39629, 0.42192, 0.25798, 0.04732, 0.052878, 0.025078, 0.0099203, 0.0099203, 0.009920320, 0.05354, 0.072535, 0.033043, 0.57687, 0.39726, 0.42415, 0.25994, 0.047237, 0.052805, 0.024953, 0.0099112, 0.0099112, 0.009911221, 0.053139, 0.072018, 0.032922, 0.56312, 0.40651, 0.42616, 0.26174, 0.047163, 0.052731, 0.024833, 0.0099017, 0.0099017, 0.009901722, 0.05324, 0.072314, 0.032852, 0.56698, 0.40809, 0.42802, 0.26296, 0.047094, 0.052672, 0.024743, 0.0098916, 0.0098916, 0.009891623, 0.052984, 0.071691, 0.032502, 0.5688, 0.40835, 0.42939, 0.26433, 0.047033, 0.052621, 0.024654, 0.0098811, 0.0098811, 0.009881124, 0.053075, 0.072245, 0.032542, 0.55917, 0.41549, 0.43074, 0.26544, 0.046973, 0.052579, 0.024576, 0.0098701, 0.0098701, 0.009870125, 0.052899, 0.073219, 0.032326, 0.57717, 0.40883, 0.43238, 0.26645, 0.046922, 0.052545, 0.024498, 0.0098586, 0.0098586, 0.009858626, 0.052927, 0.072695, 0.032286, 0.56001, 0.41737, 0.43341, 0.26761, 0.046877, 0.052522, 0.024433, 0.0098467, 0.0098467, 0.009846727, 0.052769, 0.072309, 0.032052, 0.55659, 0.42052, 0.43427, 0.2685, 0.046839, 0.052514, 0.024381, 0.0098342, 0.0098342, 0.009834228, 0.052567, 0.071681, 0.031901, 0.58324, 0.40771, 0.43537, 0.26933, 0.046809, 0.052515, 0.024332, 0.0098213, 0.0098213, 0.009821329, 0.052512, 0.071409, 0.031706, 0.55935, 0.4201, 0.4366, 0.27006, 0.04678, 0.052536, 0.024281, 0.0098079, 0.0098079, 0.009807930, 0.05257, 0.072451, 0.031773, 0.60038, 0.40082, 0.4375, 0.27078, 0.046756, 0.052566, 0.024233, 0.0097941, 0.0097941, 0.009794131, 0.052397, 0.072427, 0.031523, 0.59812, 0.40353, 0.43831, 0.27164, 0.046734, 0.052607, 0.024196, 0.0097798, 0.0097798, 0.009779832, 0.052493, 0.072668, 0.03139, 0.5401, 0.43229, 0.43888, 0.27255, 0.046715, 0.052658, 0.024157, 0.009765, 0.009765, 0.00976533, 0.05221, 0.071673, 0.031336, 0.60056, 0.40494, 0.44013, 0.27315, 0.046691, 0.052701, 0.024123, 0.0097497, 0.0097497, 0.009749734, 0.052178, 0.071612, 0.031145, 0.56314, 0.42246, 0.44092, 0.27383, 0.046666, 0.052762, 0.024081, 0.009734, 0.009734, 0.009734v5s-g
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.090561, 0.083676, 0.083902, 0.0058319, 0.047981, 0.0049234, 0.0016317, 0.07946, 0.065755, 0.078896, 0.0033297, 0.0033297, 0.0700321, 0.07336, 0.08333, 0.075127, 0.39751, 0.056787, 0.020281, 0.0077558, 0.070273, 0.063855, 0.067316, 0.0066629, 0.0066629, 0.0400322, 0.06794, 0.082012, 0.063558, 0.33755, 0.13032, 0.071825, 0.031181, 0.063522, 0.062164, 0.053643, 0.0099954, 0.0099954, 0.0100313, 0.063912, 0.079575, 0.0537, 0.35576, 0.18729, 0.14409, 0.070619, 0.059045, 0.059896, 0.044766, 0.0099978, 0.0099978, 0.00999784, 0.061241, 0.077947, 0.048031, 0.39008, 0.22238, 0.19577, 0.099902, 0.056331, 0.058283, 0.039873, 0.0099978, 0.0099978, 0.00999785, 0.059561, 0.07713, 0.044272, 0.39467, 0.27314, 0.24088, 0.13006, 0.054377, 0.057369, 0.036662, 0.0099961, 0.0099961, 0.00999616, 0.058417, 0.076726, 0.042186, 0.44821, 0.29642, 0.28312, 0.15765, 0.052918, 0.056584, 0.03407, 0.0099938, 0.0099938, 0.00999387, 0.057537, 0.075668, 0.04065, 0.44709, 0.32061, 0.3053, 0.17384, 0.051819, 0.05594, 0.032175, 0.0099911, 0.0099911, 0.00999118, 0.05707, 0.075075, 0.039091, 0.48689, 0.32388, 0.32769, 0.18949, 0.0509, 0.05535, 0.030464, 0.0099879, 0.0099879, 0.00998799, 0.056446, 0.075107, 0.038153, 0.46891, 0.3638, 0.3453, 0.20173, 0.050224, 0.054859, 0.029229, 0.0099842, 0.0099842, 0.009984210, 0.05599, 0.074498, 0.037155, 0.52217, 0.35487, 0.36348, 0.21338, 0.049652, 0.054643, 0.0284, 0.00998, 0.00998, 0.0099811, 0.055954, 0.074994, 0.036722, 0.49573, 0.37614, 0.3719, 0.22167, 0.049202, 0.054211, 0.027769, 0.0099753, 0.0099753, 0.009975312, 0.055413, 0.074568, 0.035754, 0.49957, 0.38414, 0.38323, 0.22898, 0.048783, 0.053969, 0.027122, 0.0099702, 0.0099702, 0.009970213, 0.054981, 0.07344, 0.035519, 0.52262, 0.38747, 0.39345, 0.2357, 0.048482, 0.053742, 0.026598, 0.0099645, 0.0099645, 0.009964514, 0.054688, 0.072956, 0.035128, 0.52054, 0.39699, 0.39944, 0.24077, 0.048217, 0.05351, 0.026196, 0.0099584, 0.0099584, 0.009958415, 0.054571, 0.073867, 0.034747, 0.56586, 0.38297, 0.40535, 0.24487, 0.047954, 0.05336, 0.025864, 0.0099517, 0.0099517, 0.009951716, 0.054332, 0.073118, 0.034194, 0.58583, 0.38081, 0.41132, 0.24901, 0.047765, 0.053222, 0.025573, 0.0099446, 0.0099446, 0.009944617, 0.054319, 0.073307, 0.034254, 0.5522, 0.40069, 0.41508, 0.2516, 0.047618, 0.05312, 0.02536, 0.009937, 0.009937, 0.00993718, 0.053818, 0.073281, 0.033555, 0.55216, 0.40321, 0.41761, 0.25416, 0.047509, 0.053005, 0.025187, 0.0099289, 0.0099289, 0.009928919, 0.053636, 0.072146, 0.033614, 0.58506, 0.39111, 0.42018, 0.25602, 0.047416, 0.052914, 0.025055, 0.0099203, 0.0099203, 0.009920320, 0.053749, 0.072678, 0.033477, 0.58027, 0.39386, 0.42221, 0.25771, 0.047338, 0.052827, 0.024943, 0.0099112, 0.0099112, 0.009911221, 0.053523, 0.07243, 0.03298, 0.56371, 0.40463, 0.42434, 0.25931, 0.047264, 0.052753, 0.024834, 0.0099017, 0.0099017, 0.009901722, 0.053351, 0.071941, 0.032575, 0.56743, 0.40402, 0.42539, 0.26088, 0.047201, 0.052685, 0.024746, 0.0098916, 0.0098916, 0.009891623, 0.053263, 0.072275, 0.032751, 0.56791, 0.40374, 0.42676, 0.26203, 0.047139, 0.052629, 0.024666, 0.0098811, 0.0098811, 0.009881124, 0.052973, 0.07208, 0.032323, 0.55382, 0.41207, 0.42797, 0.26304, 0.047087, 0.052588, 0.024598, 0.0098701, 0.0098701, 0.009870125, 0.052886, 0.071604, 0.03219, 0.55647, 0.41262, 0.42945, 0.26401, 0.047041, 0.052566, 0.024535, 0.0098586, 0.0098586, 0.009858626, 0.053032, 0.072313, 0.032136, 0.55879, 0.41282, 0.43074, 0.265, 0.047002, 0.052565, 0.024472, 0.0098467, 0.0098467, 0.009846727, 0.052872, 0.072334, 0.032071, 0.55561, 0.41583, 0.43179, 0.26574, 0.046965, 0.052578, 0.024414, 0.0098342, 0.0098342, 0.009834228, 0.052599, 0.071546, 0.031794, 0.58263, 0.40555, 0.43298, 0.26681, 0.046929, 0.0526, 0.024354, 0.0098213, 0.0098213, 0.009821329, 0.052618, 0.071915, 0.031572, 0.55361, 0.42016, 0.43356, 0.26752, 0.046899, 0.052632, 0.024299, 0.0098079, 0.0098079, 0.009807930, 0.05259, 0.071716, 0.031395, 0.55701, 0.42011, 0.43425, 0.26827, 0.046869, 0.052684, 0.024257, 0.0097941, 0.0097941, 0.009794131, 0.052528, 0.071275, 0.031778, 0.55873, 0.42122, 0.43573, 0.26875, 0.046841, 0.052745, 0.024209, 0.0097798, 0.0097798, 0.009779832, 0.052462, 0.072346, 0.031427, 0.56271, 0.4202, 0.43623, 0.269, 0.046816, 0.052822, 0.02416, 0.009765, 0.009765, 0.009765v5m-bs128
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.088815, 0.083758, 0.083028, 0.10967, 0.060776, 0.0084766, 0.0028998, 0.07658, 0.065096, 0.075192, 0.0033297, 0.0033297, 0.0700321, 0.07153, 0.082675, 0.069671, 0.25921, 0.12556, 0.050926, 0.021388, 0.067456, 0.063615, 0.058973, 0.0066629, 0.0066629, 0.0400322, 0.065678, 0.079942, 0.056423, 0.28735, 0.19372, 0.13283, 0.059436, 0.060568, 0.061682, 0.046271, 0.0099954, 0.0099954, 0.0100313, 0.061338, 0.078504, 0.04723, 0.40063, 0.24958, 0.22877, 0.11608, 0.055906, 0.058371, 0.037091, 0.0099978, 0.0099978, 0.00999784, 0.058455, 0.076397, 0.042069, 0.42689, 0.30947, 0.28819, 0.15312, 0.053267, 0.057035, 0.032819, 0.0099978, 0.0099978, 0.00999785, 0.056665, 0.075084, 0.038737, 0.48373, 0.33178, 0.32958, 0.18127, 0.051449, 0.05598, 0.03003, 0.0099961, 0.0099961, 0.00999616, 0.055591, 0.075298, 0.036506, 0.49513, 0.36799, 0.36491, 0.2067, 0.049934, 0.054871, 0.027925, 0.0099938, 0.0099938, 0.00999387, 0.054502, 0.073199, 0.035426, 0.49643, 0.39808, 0.38942, 0.22583, 0.048767, 0.054358, 0.026238, 0.0099911, 0.0099911, 0.00999118, 0.053876, 0.07238, 0.033299, 0.52135, 0.41381, 0.41562, 0.24501, 0.04781, 0.053518, 0.024843, 0.0099879, 0.0099879, 0.00998799, 0.053236, 0.072389, 0.032481, 0.54612, 0.41269, 0.43074, 0.25796, 0.046989, 0.053086, 0.023894, 0.0099842, 0.0099842, 0.009984210, 0.052832, 0.07241, 0.031733, 0.55228, 0.43234, 0.44671, 0.26953, 0.046382, 0.052618, 0.023047, 0.00998, 0.00998, 0.0099811, 0.05282, 0.074112, 0.031097, 0.5533, 0.44669, 0.46024, 0.28065, 0.045857, 0.052293, 0.022248, 0.0099753, 0.0099753, 0.009975312, 0.052155, 0.071912, 0.030904, 0.55584, 0.45631, 0.46781, 0.28701, 0.045427, 0.051986, 0.021778, 0.0099702, 0.0099702, 0.009970213, 0.051747, 0.072218, 0.03014, 0.60159, 0.44424, 0.47684, 0.29523, 0.045071, 0.051834, 0.021251, 0.0099645, 0.0099645, 0.009964514, 0.051199, 0.071139, 0.029753, 0.60888, 0.44745, 0.4834, 0.30013, 0.044758, 0.051615, 0.020943, 0.0099584, 0.0099584, 0.009958415, 0.051116, 0.071044, 0.029682, 0.61669, 0.45095, 0.48788, 0.30411, 0.044482, 0.051461, 0.020708, 0.0099517, 0.0099517, 0.009951716, 0.051165, 0.071407, 0.029131, 0.58867, 0.46949, 0.49298, 0.30788, 0.044266, 0.051328, 0.020452, 0.0099446, 0.0099446, 0.009944617, 0.050888, 0.070982, 0.029018, 0.60247, 0.46829, 0.49698, 0.3109, 0.044097, 0.051225, 0.020252, 0.009937, 0.009937, 0.00993718, 0.050531, 0.070728, 0.028839, 0.62038, 0.46144, 0.49886, 0.31304, 0.04396, 0.051144, 0.020106, 0.0099289, 0.0099289, 0.009928919, 0.050503, 0.070549, 0.028389, 0.61684, 0.4655, 0.50101, 0.31475, 0.043849, 0.051041, 0.019979, 0.0099203, 0.0099203, 0.009920320, 0.050645, 0.070725, 0.028245, 0.60357, 0.47478, 0.5033, 0.31661, 0.043755, 0.050947, 0.019874, 0.0099112, 0.0099112, 0.009911221, 0.050031, 0.069934, 0.027804, 0.60496, 0.47581, 0.50527, 0.31852, 0.043667, 0.050866, 0.01977, 0.0099017, 0.0099017, 0.009901722, 0.049924, 0.069962, 0.027742, 0.61439, 0.47212, 0.50718, 0.32007, 0.043592, 0.050781, 0.019677, 0.0098916, 0.0098916, 0.009891623, 0.049774, 0.070259, 0.027356, 0.62347, 0.4707, 0.50993, 0.32225, 0.043526, 0.050693, 0.019594, 0.0098811, 0.0098811, 0.009881124, 0.049478, 0.06908, 0.0275, 0.59173, 0.49002, 0.51115, 0.32364, 0.043463, 0.050619, 0.019518, 0.0098701, 0.0098701, 0.009870125, 0.049521, 0.070211, 0.027425, 0.59696, 0.4898, 0.51278, 0.32486, 0.043407, 0.050557, 0.019451, 0.0098586, 0.0098586, 0.009858626, 0.049435, 0.07008, 0.027264, 0.61438, 0.48112, 0.51381, 0.32583, 0.043351, 0.050498, 0.019382, 0.0098467, 0.0098467, 0.009846727, 0.049354, 0.070545, 0.026766, 0.6064, 0.48604, 0.51499, 0.32726, 0.043301, 0.050441, 0.019321, 0.0098342, 0.0098342, 0.009834228, 0.049133, 0.069629, 0.02687, 0.63298, 0.47289, 0.51647, 0.32861, 0.043251, 0.050395, 0.019262, 0.0098213, 0.0098213, 0.009821329, 0.049244, 0.070239, 0.026565, 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0.023495, 0.66224, 0.54308, 0.58145, 0.38531, 0.040482, 0.048251, 0.016126, 0.0084404, 0.0084404, 0.0084404yolov5s
epoch, box_loss obj_loss, cls_loss, precision,recall, mAP_0.5, 0.5:0.95, box_loss,obj_loss, cls_loss, x/lr0, x/lr1, x/lr20/299 0.514G 0.08983 0.08378 0.0827 0.2563 35 640 0.1194 0.04001 0.004798 0.001399 0.07812 0.06498 0.0771/299 12.1G 0.0736 0.08291 0.07077 0.2273 43 640 0.3335 0.09528 0.04203 0.01542 0.07008 0.06207 0.060962/299 13.9G 0.06825 0.08104 0.05821 0.2075 25 640 0.3315 0.1591 0.09668 0.03747 0.06493 0.06124 0.049353/299 13.9G 0.06433 0.07907 0.04916 0.1926 41 640 0.3415 0.2254 0.1707 0.07781 0.05992 0.05944 0.040794/299 13.9G 0.06164 0.07765 0.04405 0.1833 17 640 0.4311 0.249 0.2297 0.1112 0.05726 0.05856 0.036435/299 13.9G 0.06012 0.07706 0.04113 0.1783 36 640 0.4069 0.2922 0.2669 0.1348 0.05562 0.05759 0.033446/299 13.9G 0.05902 0.07624 0.03907 0.1743 71 640 0.4815 0.3003 0.3001 0.1566 0.05415 0.05684 0.030967/299 13.9G 0.05829 0.07524 0.03744 0.171 74 640 0.4619 0.3395 0.3239 0.1722 0.05314 0.05624 0.029398/299 13.9G 0.05778 0.07568 0.03638 0.1698 80 640 0.5126 0.3358 0.3444 0.1866 0.05225 0.05577 0.028189/299 13.9G 0.05721 0.07519 0.0356 0.168 46 640 0.521 0.3554 0.3634 0.1998 0.05158 0.05533 0.02710/299 13.9G 0.05662 0.07448 0.03487 0.166 32 640 0.5044 0.3743 0.3757 0.2083 0.05103 0.05493 0.0262311/299 13.9G 0.05629 0.07418 0.03408 0.1646 75 640 0.517 0.3836 0.3881 0.2169 0.05059 0.05473 0.0255612/299 13.9G 0.05601 0.07415 0.03357 0.1637 65 640 0.5281 0.3862 0.3939 0.2217 0.05021 0.05443 0.0250313/299 13.9G 0.05573 0.0742 0.03318 0.1631 63 640 0.5521 0.3839 0.401 0.2279 0.04981 0.05421 0.0245314/299 13.9G 0.05541 0.0737 0.03265 0.1618 37 640 0.5591 0.391 0.4085 0.2338 0.04954 0.05401 0.024115/299 13.9G 0.05519 0.07384 0.03234 0.1614 28 640 0.5382 0.4074 0.413 0.2376 0.04932 0.05388 0.0238216/299 13.9G 0.05492 0.07396 0.03209 0.161 42 640 0.5667 0.3987 0.4175 0.2409 0.04913 0.05374 0.023617/299 13.9G 0.05482 0.07353 0.0319 0.1602 43 640 0.5424 0.4097 0.4211 0.2431 0.04898 0.05364 0.0234418/299 13.9G 0.05465 0.07341 0.0314 0.1595 38 640 0.5381 0.4128 0.4235 0.2455 0.04884 0.05354 0.0232819/299 13.9G 0.05451 0.07297 0.03113 0.1586 81 640 0.5598 0.4054 0.4264 0.2473 0.04874 0.05344 0.0231420/299 13.9G 0.05443 0.07332 0.03108 0.1588 95 640 0.5749 0.3995 0.4289 0.2495 0.04866 0.05337 0.0230321/299 13.9G 0.05414 0.0727 0.03092 0.1578 26 640 0.5735 0.4018 0.4306 0.2506 0.04859 0.0533 0.0229522/299 13.9G 0.05417 0.0729 0.0308 0.1579 76 640 0.5559 0.4127 0.4322 0.2518 0.04853 0.05324 0.0228723/299 13.9G 0.05401 0.0726 0.03051 0.1571 97 640 0.5797 0.4016 0.433 0.253 0.04847 0.05319 0.0228224/299 13.9G 0.05376 0.07268 0.03013 0.1566 77 640 0.5843 0.4011 0.4338 0.2538 0.04843 0.05315 0.0227725/299 13.9G 0.05367 0.07225 0.03033 0.1563 33 640 0.5896 0.4006 0.4349 0.2547 0.04838 0.05311 0.0227226/299 13.9G 0.05358 0.07266 0.02993 0.1562 62 640 0.5885 0.4028 0.4363 0.2554 0.04834 0.05309 0.0226727/299 13.9G 0.05354 0.07231 0.03007 0.1559 80 640 0.5864 0.4041 0.4371 0.2563 0.04831 0.05307 0.0226328/299 13.9G 0.05331 0.07255 0.02963 0.1555 62 640 0.5884 0.4063 0.4386 0.2571 0.04827 0.05306 0.0225929/299 13.9G 0.05326 0.07198 0.02965 0.1549 66 640 0.5837 0.4108 0.4395 0.2579 0.04824 0.05306 0.0225530/299 13.9G 0.05322 0.07234 0.02951 0.1551 54 640 0.5867 0.4096 0.4399 0.2587 0.04821 0.05306 0.0225231/299 13.9G 0.05315 0.07232 0.02951 0.155 46 640 0.5665 0.4219 0.4407 0.259 0.04818 0.05307 0.0224832/299 13.9G 0.05315 0.07247 0.02926 0.1549 29 640 0.5637 0.4239 0.4418 0.2596 0.04815 0.05309 0.0224433/299 13.9G 0.05295 0.07147 0.02943 0.1538 26 640 0.5673 0.4228 0.443 0.2605 0.04811 0.0531 0.0224134/299 13.9G 0.05298 0.07178 0.02915 0.1539 37 640 0.5634 0.426 0.4439 0.2611 0.04808 0.05311 0.0223735/299 13.9G 0.05283 0.07181 0.02887 0.1535 34 640 0.5717 0.4236 0.4453 0.2618 0.04804 0.05311 0.0223336/299 13.9G 0.05278 0.07196 0.02874 0.1535 73 640 0.5685 0.426 0.4459 0.2625 0.048 0.05312 0.0222837/299 13.9G 0.05267 0.07162 0.0289 0.1532 103 640 0.5722 0.425 0.4473 0.2634 0.04796 0.05313 0.0222238/299 13.9G 0.05264 0.07175 0.02878 0.1532 18 640 0.5657 0.4295 0.4478 0.264 0.04792 0.05312 0.0221739/299 13.9G 0.05255 0.07183 0.0286 0.153 58 640 0.5646 0.4302 0.4492 0.2649 0.04788 0.05312 0.0221240/299 13.9G 0.05259 0.07194 0.02856 0.1531 86 640 0.5832 0.4236 0.4505 0.2658 0.04783 0.0531 0.0220541/299 13.9G 0.05239 0.07185 0.02851 0.1527 36 640 0.5801 0.4264 0.4518 0.2667 0.04778 0.05308 0.0219942/299 13.9G 0.05235 0.07162 0.02835 0.1523 73 640 0.5863 0.4254 0.4527 0.2675 0.04773 0.05306 0.0219243/299 13.9G 0.05235 0.07114 0.02857 0.1521 30 640 0.5815 0.4291 0.4545 0.2686 0.04767 0.05302 0.0218544/299 13.9G 0.0523 0.07158 0.02833 0.1522 23 640 0.5914 0.4252 0.4554 0.2698 0.04761 0.05299 0.0217845/299 13.9G 0.05239 0.07187 0.02841 0.1527 89 640 0.587 0.4306 0.4572 0.2711 0.04755 0.05295 0.021746/299 13.9G 0.05219 0.07143 0.02829 0.1519 67 640 0.5747 0.4384 0.4589 0.2721 0.04749 0.05291 0.0216347/299 13.9G 0.05213 0.0717 0.02804 0.1519 54 640 0.5909 0.4336 0.4604 0.2732 0.04743 0.05287 0.0215648/299 13.9G 0.05219 0.07187 0.02816 0.1522 47 640 0.5879 0.4372 0.4621 0.2746 0.04736 0.05282 0.0214849/299 13.9G 0.05191 0.0712 0.02806 0.1512 86 640 0.5875 0.4391 0.4636 0.2761 0.04729 0.05276 0.0214150/299 13.9G 0.05195 0.07123 0.02797 0.1512 55 640 0.5889 0.4404 0.4648 0.2773 0.04723 0.05271 0.0213351/299 13.9G 0.05188 0.07089 0.02786 0.1506 104 640 0.6019 0.4355 0.4664 0.2783 0.04716 0.05265 0.0212652/299 13.9G 0.0519 0.07134 0.02792 0.1512 48 640 0.5904 0.4421 0.4674 0.2795 0.04709 0.05261 0.0211953/299 13.9G 0.05184 0.0709 0.02775 0.1505 45 640 0.6038 0.4361 0.4684 0.2806 0.04703 0.05255 0.0211154/299 13.9G 0.05182 0.07142 0.02777 0.151 61 640 0.5985 0.4413 0.4701 0.2815 0.04697 0.0525 0.0210455/299 13.9G 0.05179 0.07121 0.02773 0.1507 56 640 0.5998 0.443 0.4713 0.2828 0.04691 0.05243 0.0209656/299 13.9G 0.05177 0.07102 0.02769 0.1505 51 640 0.6019 0.4418 0.4721 0.2842 0.04684 0.05238 0.020957/299 13.9G 0.05174 0.0713 0.02759 0.1506 60 640 0.62 0.4344 0.4737 0.285 0.04678 0.05232 0.0208358/299 13.9G 0.05167 0.07133 0.02746 0.1505 31 640 0.6075 0.4405 0.4753 0.2858 0.04673 0.05227 0.0207759/299 13.9G 0.05163 0.07157 0.02744 0.1506 84 640 0.5966 0.4474 0.4763 0.287 0.04667 0.05222 0.020760/299 13.9G 0.05152 0.07094 0.0274 0.1499 86 640 0.5997 0.4486 0.4776 0.2879 0.04662 0.05217 0.0206461/299 13.9G 0.05151 0.0712 0.02744 0.1501 72 640 0.6265 0.4381 0.4787 0.2891 0.04657 0.05212 0.0205862/299 13.9G 0.05158 0.07181 0.02733 0.1507 35 640 0.6248 0.4403 0.4802 0.29 0.04651 0.05207 0.0205263/299 13.9G 0.05141 0.07083 0.02729 0.1495 17 640 0.6215 0.4429 0.4811 0.2912 0.04646 0.05201 0.0204764/299 13.9G 0.05142 0.07106 0.02736 0.1498 61 640 0.6253 0.4408 0.482 0.2921 0.0464 0.05196 0.020465/299 13.9G 0.05138 0.07059 0.02712 0.1491 35 640 0.5948 0.4569 0.4831 0.2932 0.04635 0.05191 0.0203566/299 13.9G 0.05139 0.07051 0.02736 0.1493 73 640 0.6007 0.4564 0.4844 0.2939 0.0463 0.05186 0.020367/299 13.9G 0.05125 0.07068 0.02727 0.1492 19 640 0.6065 0.4557 0.4855 0.2951 0.04624 0.05182 0.0202468/299 13.9G 0.05118 0.07086 0.02705 0.1491 27 640 0.6141 0.4528 0.4864 0.296 0.0462 0.05178 0.0201869/299 13.9G 0.05119 0.07093 0.02711 0.1492 38 640 0.6169 0.4514 0.4878 0.2965 0.04615 0.05174 0.0201470/299 13.9G 0.05123 0.07064 0.02716 0.149 42 640 0.6163 0.4524 0.4887 0.297 0.0461 0.05169 0.0200871/299 13.9G 0.0512 0.07054 0.02688 0.1486 26 640 0.6059 0.4585 0.4896 0.2979 0.04606 0.05164 0.0200272/299 13.9G 0.05109 0.07064 0.02706 0.1488 43 640 0.6122 0.4571 0.4907 0.2987 0.04601 0.05159 0.0199773/299 13.9G 0.05119 0.07036 0.02696 0.1485 45 640 0.6171 0.4542 0.4911 0.2997 0.04596 0.05155 0.0199274/299 13.9G 0.05109 0.07071 0.02697 0.1488 87 640 0.6161 0.4564 0.4923 0.3004 0.04592 0.05151 0.0198775/299 13.9G 0.05106 0.07082 0.0268 0.1487 93 640 0.6187 0.4561 0.493 0.3008 0.04588 0.05147 0.0198276/299 13.9G 0.051 0.07073 0.02673 0.1485 54 640 0.613 0.459 0.4938 0.3015 0.04584 0.05142 0.0197777/299 13.9G 0.05089 0.07088 0.02671 0.1485 26 640 0.6137 0.4607 0.4949 0.3022 0.04579 0.05138 0.0197278/299 13.9G 0.05101 0.07076 0.02676 0.1485 44 640 0.5912 0.4766 0.496 0.3031 0.04575 0.05135 0.0196879/299 13.9G 0.05083 0.07027 0.02652 0.1476 65 640 0.5986 0.4736 0.4969 0.3036 0.04571 0.05131 0.0196580/299 13.9G 0.05089 0.07033 0.02673 0.148 28 640 0.6124 0.4689 0.498 0.3045 0.04568 0.05129 0.019681/299 13.9G 0.05093 0.0707 0.02661 0.1482 40 640 0.6135 0.4679 0.4988 0.3051 0.04565 0.05125 0.0195782/299 13.9G 0.05075 0.07079 0.02667 0.1482 34 640 0.6106 0.47 0.499 0.3056 0.04561 0.05122 0.0195383/299 13.9G 0.05073 0.07052 0.02658 0.1478 25 640 0.5994 0.4765 0.5001 0.3062 0.04558 0.05119 0.0194984/299 13.9G 0.05066 0.07025 0.02652 0.1474 43 640 0.6155 0.4676 0.4997 0.3064 0.04555 0.05116 0.0194585/299 13.9G 0.05064 0.07047 0.0265 0.1476 53 640 0.6222 0.4661 0.501 0.307 0.04552 0.05113 0.0194186/299 13.9G 0.05073 0.07078 0.02638 0.1479 44 640 0.6253 0.4657 0.5019 0.3077 0.04549 0.05111 0.0193787/299 13.9G 0.05085 0.07053 0.02638 0.1478 48 640 0.6244 0.4669 0.5038 0.3089 0.04546 0.05108 0.0193388/299 13.9G 0.05058 0.07019 0.02623 0.147 26 640 0.6253 0.467 0.5047 0.3095 0.04543 0.05106 0.0192989/299 13.9G 0.05057 0.07035 0.02644 0.1474 36 640 0.6291 0.4643 0.5052 0.3098 0.0454 0.05104 0.0192590/299 13.9G 0.05063 0.07044 0.02618 0.1473 45 640 0.6227 0.4677 0.5058 0.3105 0.04538 0.05102 0.0192191/299 13.9G 0.05046 0.07021 0.02621 0.1469 37 640 0.6151 0.4731 0.5067 0.3111 0.04535 0.05099 0.0191792/299 13.9G 0.05044 0.07041 0.0262 0.1471 81 640 0.6155 0.4746 0.508 0.312 0.04532 0.05097 0.0191393/299 13.9G 0.05041 0.06993 0.02607 0.1464 62 640 0.6212 0.4727 0.5085 0.3125 0.0453 0.05095 0.0190994/299 13.9G 0.05054 0.07044 0.02621 0.1472 44 640 0.6101 0.4793 0.5092 0.3129 0.04528 0.05093 0.0190695/299 13.9G 0.05033 0.0698 0.02606 0.1462 39 640 0.6045 0.4828 0.5093 0.3131 0.04525 0.05091 0.0190396/299 13.9G 0.05044 0.07008 0.02611 0.1466 81 640 0.6158 0.478 0.51 0.3134 0.04523 0.05089 0.01997/299 13.9G 0.05042 0.07099 0.02594 0.1473 56 640 0.612 0.4809 0.51 0.3133 0.0452 0.05087 0.0189698/299 13.9G 0.05024 0.06972 0.02603 0.146 48 640 0.63 0.4728 0.5108 0.3141 0.04518 0.05086 0.0189399/299 13.9G 0.0503 0.07 0.0261 0.1464 60 640 0.6109 0.4824 0.5115 0.3143 0.04515 0.05084 0.01888100/299 13.9G 0.05016 0.06984 0.02592 0.1459 50 640 0.6407 0.4697 0.5124 0.315 0.04513 0.05082 0.01885101/299 13.9G 0.0503 0.07008 0.02591 0.1463 63 640 0.6279 0.4771 0.5128 0.3154 0.0451 0.05081 0.01882102/299 13.9G 0.05026 0.06997 0.02589 0.1461 59 640 0.629 0.4772 0.5137 0.3158 0.04508 0.05079 0.01879103/299 13.9G 0.05017 0.07046 0.02587 0.1465 30 640 0.638 0.4745 0.5142 0.316 0.04506 0.05077 0.01877104/299 13.9G 0.05025 0.07051 0.02572 0.1465 53 640 0.6317 0.4763 0.5143 0.3166 0.04503 0.05075 0.01874105/299 13.9G 0.05015 0.06961 0.02557 0.1453 61 640 0.6316 0.4775 0.5152 0.3175 0.045 0.05073 0.01871106/299 13.9G 0.0501 0.07019 0.02576 0.146 30 640 0.6228 0.4834 0.516 0.3177 0.04498 0.05071 0.01869107/299 13.9G 0.0502 0.06981 0.02575 0.1458 17 640 0.6276 0.4798 0.5162 0.3182 0.04496 0.0507 0.01868108/299 13.9G 0.05004 0.07002 0.02569 0.1457 68 640 0.6091 0.4907 0.5164 0.3184 0.04493 0.05069 0.01867109/299 13.9G 0.04992 0.06985 0.0255 0.1453 56 640 0.6029 0.4943 0.5166 0.3192 0.04491 0.05067 0.01864110/299 13.9G 0.05001 0.06975 0.02565 0.1454 35 640 0.6214 0.4847 0.5173 0.3196 0.04489 0.05066 0.01862111/299 13.9G 0.05002 0.0702 0.02554 0.1458 41 640 0.6133 0.4906 0.5177 0.3197 0.04487 0.05065 0.0186112/299 13.9G 0.04995 0.06921 0.02565 0.1448 41 640 0.6152 0.4897 0.5187 0.3206 0.04485 0.05064 0.01859113/299 13.9G 0.0499 0.06982 0.02538 0.1451 71 640 0.6226 0.487 0.5191 0.3205 0.04482 0.05063 0.01856114/299 13.9G 0.04982 0.06954 0.02548 0.1448 81 640 0.6252 0.4857 0.5195 0.3208 0.0448 0.05062 0.01853115/299 13.9G 0.04997 0.06978 0.02545 0.1452 76 640 0.6257 0.486 0.5197 0.321 0.04478 0.05061 0.01851116/299 13.9G 0.04984 0.06965 0.02557 0.1451 42 640 0.6258 0.487 0.5206 0.3213 0.04476 0.05061 0.01849117/299 13.9G 0.04983 0.07003 0.02536 0.1452 31 640 0.6384 0.4796 0.5208 0.322 0.04475 0.05059 0.01848118/299 13.9G 0.04978 0.0695 0.02539 0.1447 38 640 0.6274 0.4866 0.5213 0.3226 0.04473 0.05058 0.01846119/299 13.9G 0.04963 0.06953 0.02525 0.1444 30 640 0.6304 0.4853 0.5215 0.323 0.04471 0.05058 0.01843120/299 13.9G 0.04971 0.06967 0.02537 0.1448 29 640 0.6465 0.4776 0.5217 0.3234 0.0447 0.05057 0.01841121/299 13.9G 0.04979 0.06958 0.02512 0.1445 37 640 0.6441 0.4775 0.5217 0.3235 0.04469 0.05056 0.0184122/299 13.9G 0.04993 0.06982 0.02532 0.1451 57 640 0.6404 0.479 0.5218 0.3239 0.04467 0.05055 0.01838123/299 13.9G 0.04958 0.06963 0.02497 0.1442 64 640 0.6192 0.4901 0.5222 0.324 0.04465 0.05054 0.01836124/299 13.9G 0.04956 0.06996 0.02511 0.1446 70 640 0.619 0.4912 0.5229 0.3244 0.04464 0.05053 0.01834125/299 13.9G 0.04962 0.06923 0.02494 0.1438 38 640 0.6195 0.4927 0.5235 0.3247 0.04462 0.05052 0.01833126/299 13.9G 0.04964 0.06938 0.02518 0.1442 36 640 0.6149 0.4959 0.5243 0.3252 0.0446 0.05051 0.01831127/299 13.9G 0.04964 0.06994 0.02506 0.1446 19 640 0.614 0.4964 0.5246 0.3258 0.04459 0.0505 0.01829128/299 13.9G 0.04955 0.06917 0.02492 0.1436 19 640 0.6218 0.4921 0.5255 0.3263 0.04457 0.0505 0.01825129/299 13.9G 0.04957 0.06944 0.02487 0.1439 79 640 0.6164 0.4975 0.5264 0.3263 0.04455 0.05049 0.01823130/299 13.9G 0.04945 0.06973 0.02481 0.144 62 640 0.6147 0.4984 0.5266 0.3269 0.04453 0.05048 0.01821131/299 13.9G 0.04948 0.0693 0.02493 0.1437 34 640 0.6257 0.4916 0.5269 0.3269 0.04452 0.05047 0.01819132/299 13.9G 0.04933 0.06903 0.0247 0.1431 94 640 0.6251 0.4918 0.5273 0.3272 0.0445 0.05046 0.01818133/299 13.9G 0.04933 0.06906 0.0247 0.1431 73 640 0.6171 0.4963 0.5274 0.3281 0.04447 0.05045 0.01815134/299 13.9G 0.04941 0.06976 0.02479 0.144 70 640 0.6161 0.4967 0.5276 0.3285 0.04445 0.05043 0.01813135/299 13.9G 0.04931 0.06943 0.02469 0.1434 49 640 0.627 0.4911 0.5279 0.3288 0.04443 0.05042 0.01811136/299 13.9G 0.04944 0.0695 0.02473 0.1437 72 640 0.6436 0.4825 0.5282 0.3291 0.04441 0.0504 0.01809137/299 13.9G 0.04926 0.06901 0.02465 0.1429 51 640 0.645 0.4823 0.5287 0.3296 0.0444 0.05038 0.01808138/299 13.9G 0.04932 0.06938 0.02476 0.1435 60 640 0.6409 0.4852 0.5289 0.3298 0.04439 0.05037 0.01806139/299 13.9G 0.04929 0.0694 0.02463 0.1433 60 640 0.6259 0.4928 0.5297 0.3298 0.04437 0.05034 0.01804140/299 13.9G 0.04934 0.06922 0.02455 0.1431 120 640 0.6344 0.4888 0.5301 0.3303 0.04435 0.05033 0.01802141/299 13.9G 0.04924 0.06931 0.02464 0.1432 35 640 0.6347 0.4891 0.5306 0.3306 0.04433 0.05032 0.01801142/299 13.9G 0.04906 0.06904 0.02447 0.1426 58 640 0.6469 0.4829 0.5308 0.3312 0.04432 0.05031 0.01799143/299 13.9G 0.04917 0.06905 0.02435 0.1426 81 640 0.625 0.4941 0.531 0.3315 0.04431 0.05029 0.01798144/299 13.9G 0.04919 0.06887 0.02427 0.1423 45 640 0.6417 0.4864 0.5316 0.332 0.04429 0.05029 0.01796145/299 13.9G 0.04917 0.06891 0.0244 0.1425 28 640 0.646 0.4843 0.5314 0.332 0.04428 0.05027 0.01794146/299 13.9G 0.04912 0.06936 0.02445 0.1429 51 640 0.6232 0.4977 0.5316 0.3322 0.04427 0.05026 0.01794147/299 13.9G 0.04904 0.06917 0.02426 0.1425 22 640 0.6438 0.4869 0.5318 0.3323 0.04426 0.05025 0.01793148/299 13.9G 0.04906 0.06916 0.0244 0.1426 39 640 0.6556 0.4817 0.5322 0.3323 0.04425 0.05024 0.01792149/299 13.9G 0.04902 0.06903 0.02424 0.1423 38 640 0.6462 0.4871 0.5324 0.3323 0.04423 0.05023 0.01791150/299 13.9G 0.04899 0.06909 0.02422 0.1423 46 640 0.6337 0.494 0.5325 0.3324 0.04422 0.05022 0.01789151/299 13.9G 0.04903 0.06961 0.02415 0.1428 41 640 0.6375 0.4921 0.5327 0.3325 0.0442 0.05022 0.01788152/299 13.9G 0.04908 0.06921 0.02427 0.1426 47 640 0.6348 0.4936 0.5329 0.3327 0.0442 0.05021 0.01787153/299 13.9G 0.04887 0.06909 0.02405 0.142 106 640 0.6533 0.483 0.5331 0.3333 0.04419 0.0502 0.01786154/299 13.9G 0.04895 0.06891 0.02404 0.1419 65 640 0.6428 0.4896 0.5338 0.3338 0.04417 0.05019 0.01784155/299 13.9G 0.04889 0.06886 0.02401 0.1418 51 640 0.6619 0.4801 0.5342 0.3343 0.04415 0.05017 0.01783156/299 13.9G 0.04885 0.06849 0.02394 0.1413 60 640 0.6539 0.4861 0.535 0.3348 0.04414 0.05016 0.01782157/299 13.9G 0.04871 0.06865 0.02396 0.1413 52 640 0.6521 0.4879 0.5351 0.3352 0.04413 0.05016 0.0178158/299 13.9G 0.04867 0.06844 0.02382 0.1409 29 640 0.6486 0.4892 0.5349 0.3354 0.04412 0.05015 0.01779159/299 13.9G 0.04879 0.06946 0.02388 0.1421 50 640 0.6634 0.4824 0.5354 0.3355 0.04411 0.05014 0.01778160/299 13.9G 0.04865 0.06895 0.02383 0.1414 34 640 0.6586 0.4849 0.5354 0.3357 0.0441 0.05013 0.01777161/299 13.9G 0.04873 0.06927 0.02371 0.1417 52 640 0.6594 0.485 0.5357 0.3357 0.04409 0.05013 0.01775162/299 13.9G 0.04861 0.06876 0.02369 0.141 33 640 0.6413 0.4939 0.5354 0.3362 0.04408 0.05013 0.01773163/299 13.9G 0.04857 0.06862 0.02371 0.1409 13 640 0.6578 0.4855 0.5356 0.3363 0.04406 0.05012 0.01771164/299 13.9G 0.04859 0.06874 0.02356 0.1409 48 640 0.6417 0.4947 0.5362 0.3366 0.04405 0.05012 0.01769165/299 13.9G 0.04851 0.06865 0.02355 0.1407 21 640 0.6484 0.4914 0.5369 0.3366 0.04404 0.05011 0.01767166/299 13.9G 0.04848 0.06861 0.02344 0.1405 46 640 0.6631 0.4844 0.5374 0.3368 0.04403 0.0501 0.01766167/299 13.9G 0.04848 0.06861 0.02365 0.1407 33 640 0.6623 0.4858 0.5378 0.337 0.04401 0.05009 0.01765168/299 13.9G 0.04852 0.0687 0.0236 0.1408 29 640 0.6438 0.4965 0.5381 0.3369 0.044 0.05008 0.01764169/299 13.9G 0.0485 0.06875 0.02348 0.1407 48 640 0.6591 0.4891 0.5382 0.337 0.04399 0.05007 0.01763170/299 13.9G 0.0484 0.06853 0.02337 0.1403 37 640 0.6628 0.4871 0.5381 0.3373 0.04398 0.05006 0.01762171/299 13.9G 0.04844 0.06891 0.02338 0.1407 92 640 0.6126 0.5132 0.5384 0.3373 0.04397 0.05005 0.0176172/299 13.9G 0.04844 0.0687 0.02338 0.1405 24 640 0.6146 0.5128 0.538 0.3373 0.04397 0.05005 0.01758repvgg-v5s1
这个部分我将只讲C3-Bottleneck的cv1-cv2进行了替换，其他部分没有替换。效果提升的不是很明显。
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#batchsize=128 v5s 结构重参数化bottleneck #95M 参数量 FLOPs 22.4
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091325, 0.084251, 0.083019, 0.045864, 0.040693, 0.0064491, 0.0018495, 0.082129, 0.062835, 0.077701, 0.0033297, 0.0033297, 0.0700321, 0.074439, 0.083221, 0.071887, 0.31976, 0.085781, 0.036077, 0.013726, 0.070137, 0.061606, 0.063021, 0.0066629, 0.0066629, 0.0400322, 0.068349, 0.080858, 0.058965, 0.31089, 0.15308, 0.099369, 0.042348, 0.064392, 0.059422, 0.049325, 0.0099954, 0.0099954, 0.0100313, 0.063965, 0.078798, 0.04917, 0.32458, 0.22688, 0.18459, 0.088395, 0.059321, 0.057765, 0.040668, 0.0099978, 0.0099978, 0.00999784, 0.061199, 0.077382, 0.043838, 0.38715, 0.25856, 0.23561, 0.11888, 0.056412, 0.056409, 0.035993, 0.0099978, 0.0099978, 0.00999785, 0.059543, 0.076267, 0.040438, 0.41801, 0.31225, 0.28473, 0.15017, 0.05461, 0.055364, 0.032676, 0.0099961, 0.0099961, 0.00999616, 0.058327, 0.07549, 0.038161, 0.46336, 0.31775, 0.31134, 0.16864, 0.053299, 0.0548, 0.030894, 0.0099938, 0.0099938, 0.00999387, 0.057373, 0.075205, 0.036591, 0.49252, 0.33666, 0.34447, 0.19211, 0.052136, 0.053898, 0.028695, 0.0099911, 0.0099911, 0.00999118, 0.056636, 0.074615, 0.035219, 0.52364, 0.34815, 0.36206, 0.20422, 0.051213, 0.053404, 0.027527, 0.0099879, 0.0099879, 0.00998799, 0.05602, 0.073934, 0.034359, 0.50992, 0.38009, 0.38225, 0.21813, 0.05043, 0.052847, 0.026286, 0.0099842, 0.0099842, 0.009984210, 0.055668, 0.073962, 0.033563, 0.51525, 0.38478, 0.3973, 0.22897, 0.049851, 0.052506, 0.025292, 0.00998, 0.00998, 0.0099811, 0.055276, 0.073709, 0.032925, 0.54625, 0.38502, 0.40782, 0.23643, 0.049367, 0.05212, 0.02475, 0.0099753, 0.0099753, 0.009975312, 0.054877, 0.07297, 0.032321, 0.55703, 0.39541, 0.41659, 0.24355, 0.048975, 0.051898, 0.024132, 0.0099702, 0.0099702, 0.009970213, 0.054596, 0.073085, 0.031765, 0.54956, 0.40851, 0.42707, 0.25115, 0.048565, 0.051643, 0.023694, 0.0099645, 0.0099645, 0.009964514, 0.05431, 0.073009, 0.03143, 0.56696, 0.41039, 0.4336, 0.2563, 0.048272, 0.051451, 0.023302, 0.0099584, 0.0099584, 0.009958415, 0.05406, 0.072771, 0.030999, 0.56164, 0.41919, 0.43919, 0.26051, 0.048036, 0.051295, 0.023009, 0.0099517, 0.0099517, 0.009951716, 0.053834, 0.072276, 0.030678, 0.57196, 0.42093, 0.44328, 0.26414, 0.04783, 0.051172, 0.022729, 0.0099446, 0.0099446, 0.009944617, 0.053577, 0.072335, 0.030367, 0.58215, 0.419, 0.44688, 0.2668, 0.047664, 0.051044, 0.022535, 0.009937, 0.009937, 0.00993718, 0.053448, 0.072006, 0.030096, 0.575, 0.42522, 0.44958, 0.26966, 0.047528, 0.050944, 0.022371, 0.0099289, 0.0099289, 0.009928919, 0.053259, 0.071957, 0.029836, 0.5959, 0.41816, 0.45258, 0.27204, 0.047402, 0.050845, 0.022239, 0.0099203, 0.0099203, 0.009920320, 0.053077, 0.071917, 0.029526, 0.60879, 0.41392, 0.45441, 0.27368, 0.047301, 0.050761, 0.02214, 0.0099112, 0.0099112, 0.009911221, 0.052961, 0.071599, 0.029387, 0.56503, 0.43677, 0.45631, 0.27456, 0.04722, 0.05069, 0.022046, 0.0099017, 0.0099017, 0.009901722, 0.052864, 0.071617, 0.029193, 0.58445, 0.42799, 0.45746, 0.2759, 0.047137, 0.050628, 0.021959, 0.0098916, 0.0098916, 0.009891623, 0.052629, 0.071329, 0.028944, 0.59464, 0.42518, 0.45866, 0.277, 0.047065, 0.050569, 0.021889, 0.0098811, 0.0098811, 0.009881124, 0.052497, 0.071371, 0.028764, 0.57254, 0.43689, 0.45942, 0.278, 0.047001, 0.050518, 0.021832, 0.0098701, 0.0098701, 0.009870125, 0.052393, 0.071428, 0.028541, 0.56765, 0.44037, 0.46032, 0.2787, 0.046941, 0.050474, 0.021784, 0.0098586, 0.0098586, 0.009858626, 0.052246, 0.071214, 0.028439, 0.5729, 0.43879, 0.46132, 0.27977, 0.046887, 0.050435, 0.02172, 0.0098467, 0.0098467, 0.009846727, 0.052175, 0.07089, 0.028303, 0.59449, 0.42917, 0.46239, 0.28053, 0.046835, 0.050403, 0.021667, 0.0098342, 0.0098342, 0.009834228, 0.052075, 0.070701, 0.028244, 0.60295, 0.42625, 0.46385, 0.28191, 0.046789, 0.050377, 0.021617, 0.0098213, 0.0098213, 0.009821329, 0.051997, 0.071067, 0.028108, 0.58521, 0.43614, 0.46475, 0.28292, 0.046745, 0.050361, 0.02157, 0.0098079, 0.0098079, 0.009807930, 0.051922, 0.071, 0.027901, 0.61236, 0.42383, 0.46511, 0.28362, 0.046701, 0.050348, 0.021524, 0.0097941, 0.0097941, 0.009794131, 0.051872, 0.070737, 0.027774, 0.62071, 0.42165, 0.46607, 0.28453, 0.046654, 0.05034, 0.021475, 0.0097798, 0.0097798, 0.009779832, 0.05168, 0.070582, 0.027777, 0.58882, 0.43789, 0.46703, 0.28551, 0.046607, 0.050339, 0.02142, 0.009765, 0.009765, 0.00976533, 0.051612, 0.070762, 0.027565, 0.59094, 0.43816, 0.46795, 0.28637, 0.046563, 0.050342, 0.02138, 0.0097497, 0.0097497, 0.009749734, 0.051544, 0.070445, 0.027422, 0.58, 0.44565, 0.46896, 0.28706, 0.046517, 0.050339, 0.021334, 0.009734, 0.009734, 0.00973435, 0.051499, 0.070687, 0.027361, 0.60279, 0.43692, 0.46993, 0.28788, 0.046476, 0.050338, 0.02129, 0.0097178, 0.0097178, 0.009717836, 0.051421, 0.070385, 0.027363, 0.57595, 0.45075, 0.47137, 0.28923, 0.046433, 0.050337, 0.021241, 0.0097011, 0.0097011, 0.009701137, 0.051288, 0.070366, 0.027199, 0.57659, 0.4512, 0.47223, 0.28996, 0.046385, 0.050332, 0.021189, 0.009684, 0.009684, 0.00968438, 0.051249, 0.070556, 0.027186, 0.58312, 0.44984, 0.47376, 0.29134, 0.046333, 0.050325, 0.021127, 0.0096664, 0.0096664, 0.009666439, 0.051224, 0.070369, 0.026999, 0.56718, 0.46055, 0.47543, 0.29283, 0.046283, 0.050315, 0.02107, 0.0096484, 0.0096484, 0.009648440, 0.051065, 0.07025, 0.026815, 0.57623, 0.45931, 0.47704, 0.29384, 0.04624, 0.050295, 0.021016, 0.0096299, 0.0096299, 0.0096299repvgg-v5s2-not-fuse
这个部分是只将卷积大小为3x3，并且stride=1的进行替换，对于其他大小的卷积或者下采样的Conv没有替换。
- Model Summary: 529 layers, 18323901 parameters, 18323901 gradients, 22.1 GFLOPs bs=256
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.092004, 0.084015, 0.082964, 0.029228, 0.040308, 0.0036937, 0.0011781, 0.08216, 0.064867, 0.077113, 0.0033297, 0.0033297, 0.0700321, 0.074385, 0.083075, 0.071424, 0.36081, 0.083457, 0.032672, 0.010698, 0.070906, 0.063654, 0.062175, 0.0066629, 0.0066629, 0.0400322, 0.068213, 0.080396, 0.058372, 0.33255, 0.16075, 0.099448, 0.042357, 0.064713, 0.060401, 0.051163, 0.0099954, 0.0099954, 0.0100313, 0.063901, 0.079088, 0.048938, 0.37051, 0.21196, 0.17536, 0.082413, 0.059104, 0.057996, 0.04138, 0.0099978, 0.0099978, 0.00999784, 0.06108, 0.077382, 0.043561, 0.40702, 0.26596, 0.23681, 0.12011, 0.056339, 0.056411, 0.035908, 0.0099978, 0.0099978, 0.00999785, 0.059405, 0.076233, 0.040388, 0.41548, 0.3136, 0.28489, 0.15177, 0.054522, 0.055313, 0.032694, 0.0099961, 0.0099961, 0.00999616, 0.058182, 0.075428, 0.038158, 0.4367, 0.33047, 0.3094, 0.1678, 0.05316, 0.054771, 0.030924, 0.0099938, 0.0099938, 0.00999387, 0.05735, 0.075031, 0.036529, 0.51722, 0.33262, 0.3434, 0.18961, 0.052, 0.053987, 0.028712, 0.0099911, 0.0099911, 0.00999118, 0.056641, 0.07458, 0.035336, 0.53165, 0.34665, 0.36402, 0.20534, 0.051109, 0.05336, 0.027521, 0.0099879, 0.0099879, 0.00998799, 0.05608, 0.074169, 0.034309, 0.51649, 0.3766, 0.38203, 0.21764, 0.050346, 0.052864, 0.026372, 0.0099842, 0.0099842, 0.009984210, 0.055596, 0.073769, 0.033533, 0.52767, 0.39115, 0.39924, 0.22918, 0.049731, 0.052498, 0.02528, 0.00998, 0.00998, 0.0099811, 0.055214, 0.073445, 0.032875, 0.51122, 0.40731, 0.40802, 0.23782, 0.049252, 0.052116, 0.024745, 0.0099753, 0.0099753, 0.009975312, 0.054828, 0.073024, 0.032236, 0.545, 0.3989, 0.41737, 0.24483, 0.048801, 0.051849, 0.024273, 0.0099702, 0.0099702, 0.009970213, 0.054548, 0.073003, 0.031797, 0.53235, 0.41676, 0.42498, 0.25128, 0.048508, 0.051585, 0.023823, 0.0099645, 0.0099645, 0.009964514, 0.054265, 0.072806, 0.031344, 0.54557, 0.41953, 0.43157, 0.25618, 0.048195, 0.051413, 0.023407, 0.0099584, 0.0099584, 0.009958415, 0.054041, 0.07252, 0.030969, 0.56267, 0.4155, 0.43702, 0.26028, 0.047964, 0.051246, 0.023063, 0.0099517, 0.0099517, 0.0099517
repvgg-v5s2-fuse
在上次 repvgg-v5s2修改中，只修改了kernel_size=3，stride=1的卷积，对于其他大小的卷积或者stride!=1的卷积，没有进行fuse_conv_bn操作，而在这次实验中则完善了这一操作！这个BottleNeck里面的cv1,cv2的kernel_size都是3，但是yolov5s里面的是k1=1与k2=3，所以这个需要变一下
#experimental.py
# detect.py加载pt文件时，调用了yolo.py中的fuse操作（下面），对conv与bn进行了fusedef attempt_load(weights, map_location=None, inplace=True, fuse=True):from models.yolo import Detect, Model# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=amodel = Ensemble()for w in weights if isinstance(weights, list) else [weights]:ckpt = torch.load(attempt_download(w), map_location=map_location) # loadif fuse:model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 modelelse:model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse#yolo.pydef fuse(self): # fuse model Conv2d() + BatchNorm2d() layersLOGGER.info('Fusing layers... ')for m in self.model.modules():if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):m.brb_rep = fuse_conv_and_bn(m.brb_rep, m.bn) # update convdelattr(m, 'bn') # remove batchnormm.forward = m.forward_fuse # update forwardself.info()return self#torch_utils.py
#这个可以根据自己的需要进行修改，比如conv.in_channels修改成conv.conv.in_channels，具体要看自己的网络模块是什么样的。def fuse_conv_and_bn(conv, bn):# Fuse convolution and batchnorm layers = nn.Conv2d(conv.in_channels,conv.out_channels,kernel_size=conv.kernel_size,stride=conv.stride,padding=conv.padding,groups=conv.groups,bias=True).requires_grad_(False).to(conv.weight.device)# prepare filtersw_conv = conv.weight.clone().view(conv.out_channels, -1)w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))# prepare spatial biasb_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.biasb_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)return fusedconvclass Conv(nn.Module):def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True,deploy=False): # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()......def forward(self, inputs):#如果不进行_switch_to_deploy,下面两个return就足够了if (self.deploy):#Conv._switch_to_deploy 会改变deployif hasattr(self,'bn'):#说明不是结构重参数化return self.act(self.bn(self.brb_rep(inputs)))else:#说明是结构重参数化，没有bn操作return self.act(self.brb_rep(inputs))#下面是为了不进行_switch_to_deploy，进行的forward操作if (self.brb_identity == None):identity_out = 0else:identity_out = self.brb_identity(inputs)return self.act(self.brb_1x1(inputs) + self.brb_3x3(inputs) + identity_out)def forward_fuse(self, inputs):if (self.deploy):#Conv._switch_to_deploy 会改变deploy# print(self.brb_rep(torch.randn(1,3,4,4)).shape)# print('self.brb_rep.kernel_size',self.brb_rep.kernel_size)return self.act(self.brb_rep(inputs))if (self.brb_identity == None):identity_out = 0else:identity_out = self.brb_identity(inputs)return self.act(self.brb_1x1(inputs) + self.brb_3x3(inputs) + identity_out)记录
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091684, 0.083873, 0.083003, 0.11889, 0.036322, 0.0047963, 0.0013873, 0.080382, 0.062765, 0.077113, 0.0033297, 0.0033297, 0.0700321, 0.074254, 0.08325, 0.071897, 0.28534, 0.1069, 0.038758, 0.014097, 0.069871, 0.060908, 0.063017, 0.0066629, 0.0066629, 0.0400322, 0.068055, 0.080653, 0.058831, 0.30226, 0.15678, 0.097203, 0.041169, 0.064179, 0.060451, 0.049972, 0.0099954, 0.0099954, 0.0100313, 0.063864, 0.078561, 0.049357, 0.37208, 0.20302, 0.17114, 0.081694, 0.059796, 0.057443, 0.041513, 0.0099978, 0.0099978, 0.00999784, 0.061138, 0.077359, 0.04392, 0.34799, 0.27356, 0.23034, 0.11579, 0.056402, 0.056551, 0.036675, 0.0099978, 0.0099978, 0.00999785, 0.059469, 0.076227, 0.040648, 0.45373, 0.2866, 0.27911, 0.14818, 0.054577, 0.055503, 0.033043, 0.0099961, 0.0099961, 0.00999616, 0.058296, 0.075598, 0.038351, 0.45885, 0.32383, 0.31248, 0.17017, 0.053094, 0.054646, 0.030785, 0.0099938, 0.0099938, 0.00999387, 0.057482, 0.075143, 0.036739, 0.48984, 0.34399, 0.34029, 0.18835, 0.052042, 0.053939, 0.028925, 0.0099911, 0.0099911, 0.00999118, 0.056705, 0.074649, 0.035441, 0.4915, 0.3654, 0.36197, 0.20541, 0.051114, 0.05332, 0.027579, 0.0099879, 0.0099879, 0.00998799, 0.056144, 0.074082, 0.034403, 0.52628, 0.37434, 0.38172, 0.21816, 0.050361, 0.052842, 0.026346, 0.0099842, 0.0099842, 0.009984210, 0.055666, 0.073836, 0.033553, 0.51832, 0.39206, 0.39602, 0.2287, 0.049761, 0.052426, 0.025471, 0.00998, 0.00998, 0.0099811, 0.055231, 0.073437, 0.032997, 0.56388, 0.37978, 0.40593, 0.23718, 0.04923, 0.0521, 0.024748, 0.0099753, 0.0099753, 0.009975312, 0.054889, 0.073394, 0.032308, 0.57626, 0.38826, 0.41714, 0.24481, 0.048803, 0.051869, 0.024245, 0.0099702, 0.0099702, 0.009970213, 0.054554, 0.072821, 0.031894, 0.53876, 0.41098, 0.42532, 0.25248, 0.048478, 0.051596, 0.023739, 0.0099645, 0.0099645, 0.009964514, 0.054293, 0.072961, 0.031428, 0.55517, 0.41513, 0.43209, 0.25723, 0.048174, 0.051402, 0.02335, 0.0099584, 0.0099584, 0.009958415, 0.054051, 0.072379, 0.03106, 0.5711, 0.4148, 0.43923, 0.26223, 0.047915, 0.051225, 0.023025, 0.0099517, 0.0099517, 0.009951716, 0.053788, 0.072349, 0.030638, 0.56422, 0.42605, 0.44355, 0.26615, 0.047721, 0.051081, 0.022759, 0.0099446, 0.0099446, 0.009944617, 0.053498, 0.072054, 0.030305, 0.58218, 0.4219, 0.44725, 0.26785, 0.047562, 0.050984, 0.022552, 0.009937, 0.009937, 0.00993718, 0.053371, 0.071944, 0.030003, 0.57388, 0.42997, 0.45016, 0.27031, 0.04744, 0.050874, 0.022405, 0.0099289, 0.0099289, 0.009928919, 0.053229, 0.071684, 0.029826, 0.58115, 0.42835, 0.4526, 0.27211, 0.047332, 0.050791, 0.02227, 0.0099203, 0.0099203, 0.009920320, 0.053079, 0.071805, 0.029529, 0.58502, 0.42681, 0.45375, 0.27357, 0.047245, 0.050707, 0.022169, 0.0099112, 0.0099112, 0.009911221, 0.052902, 0.071675, 0.029337, 0.58125, 0.43068, 0.45569, 0.27526, 0.047169, 0.050641, 0.022076, 0.0099017, 0.0099017, 0.009901722, 0.052779, 0.071798, 0.029114, 0.57623, 0.43864, 0.45683, 0.27636, 0.047096, 0.050588, 0.021995, 0.0098916, 0.0098916, 0.009891623, 0.052688, 0.07148, 0.028993, 0.5592, 0.44757, 0.45836, 0.27782, 0.047032, 0.050533, 0.021918, 0.0098811, 0.0098811, 0.009881124, 0.052492, 0.071561, 0.028844, 0.56807, 0.4431, 0.45924, 0.27891, 0.046975, 0.050497, 0.021851, 0.0098701, 0.0098701, 0.009870125, 0.052359, 0.071399, 0.028671, 0.5645, 0.44534, 0.46058, 0.28024, 0.046923, 0.05046, 0.021784, 0.0098586, 0.0098586, 0.009858626, 0.052267, 0.071078, 0.028537, 0.56427, 0.44617, 0.46203, 0.28136, 0.046877, 0.05043, 0.021728, 0.0098467, 0.0098467, 0.009846727, 0.052167, 0.071205, 0.02834, 0.56993, 0.44432, 0.463, 0.28212, 0.046831, 0.050411, 0.021681, 0.0098342, 0.0098342, 0.009834228, 0.052017, 0.070836, 0.028275, 0.58049, 0.44164, 0.46401, 0.28307, 0.046781, 0.050408, 0.021631, 0.0098213, 0.0098213, 0.009821329, 0.051979, 0.070958, 0.028054, 0.57612, 0.44456, 0.46563, 0.28385, 0.046736, 0.050405, 0.021588, 0.0098079, 0.0098079, 0.009807930, 0.051819, 0.070996, 0.028032, 0.58498, 0.4407, 0.46687, 0.28457, 0.046697, 0.050407, 0.021544, 0.0097941, 0.0097941, 0.009794131, 0.051814, 0.070874, 0.027923, 0.59036, 0.43892, 0.46802, 0.28548, 0.046656, 0.050411, 0.021493, 0.0097798, 0.0097798, 0.009779832, 0.051686, 0.070831, 0.027725, 0.57675, 0.44805, 0.46862, 0.28645, 0.046615, 0.050426, 0.021445, 0.009765, 0.009765, 0.00976533, 0.051667, 0.070634, 0.0277, 0.58037, 0.44705, 0.46963, 0.28724, 0.046574, 0.050447, 0.021389, 0.0097497, 0.0097497, 0.009749734, 0.051521, 0.070458, 0.027497, 0.58719, 0.44374, 0.47044, 0.28792, 0.046535, 0.050461, 0.021332, 0.009734, 0.009734, 0.00973435, 0.051506, 0.070662, 0.027441, 0.5885, 0.44439, 0.47186, 0.28942, 0.046491, 0.050471, 0.021269, 0.0097178, 0.0097178, 0.009717836, 0.051418, 0.07035, 0.027413, 0.59529, 0.44237, 0.47313, 0.29034, 0.04645, 0.050473, 0.021213, 0.0097011, 0.0097011, 0.009701137, 0.051342, 0.07035, 0.027347, 0.59307, 0.4456, 0.47436, 0.2915, 0.046409, 0.050474, 0.021146, 0.009684, 0.009684, 0.00968438, 0.051249, 0.070294, 0.027215, 0.60617, 0.44072, 0.47558, 0.29213, 0.046363, 0.050472, 0.02108, 0.0096664, 0.0096664, 0.0096664bs128-v5m-only3-repvgg-失败
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.091429, 0.083921, 0.083333, 0.18123, 0.037406, 0.0054993, 0.0017863, 0.080575, 0.065993, 0.077819, 0.0033297, 0.0033297, 0.0700321, 0.07315, 0.083372, 0.072354, 0.34694, 0.080206, 0.043477, 0.016405, 0.068264, 0.064052, 0.060966, 0.0066629, 0.0066629, 0.0400322, 0.066702, 0.081285, 0.059143, 0.35, 0.15084, 0.10672, 0.04587, 0.062273, 0.061312, 0.048442, 0.0099954, 0.0099954, 0.0100313, 0.062169, 0.079481, 0.049128, 0.30166, 0.24924, 0.1853, 0.091003, 0.057319, 0.059455, 0.039904, 0.0099978, 0.0099978, 0.00999784, 0.059529, 0.076886, 0.043603, 0.40681, 0.28908, 0.25994, 0.13575, 0.054467, 0.057602, 0.035494, 0.0099978, 0.0099978, 0.00999785, 0.057298, 0.076357, 0.040525, 0.48307, 0.30996, 0.30937, 0.16804, 0.052252, 0.056752, 0.031491, 0.0099961, 0.0099961, 0.00999616, 0.056366, 0.075171, 0.038025, 0.47274, 0.35327, 0.34361, 0.19272, 0.050701, 0.055754, 0.02923, 0.0099938, 0.0099938, 0.00999387, 0.055512, 0.075627, 0.036506, 0.51957, 0.36741, 0.37435, 0.21306, 0.049501, 0.054915, 0.027218, 0.0099911, 0.0099911, 0.00999118, 0.054382, 0.072708, 0.034843, 0.54941, 0.37464, 0.39539, 0.22905, 0.048637, 0.054252, 0.025949, 0.0099879, 0.0099879, 0.00998799, 0.054007, 0.073536, 0.033998, 0.53968, 0.39345, 0.41214, 0.24421, 0.047677, 0.05369, 0.024703, 0.0099842, 0.0099842, 0.009984210, 0.053224, 0.072996, 0.03322, 0.53395, 0.41462, 0.4247, 0.2539, 0.047103, 0.053297, 0.023964, 0.00998, 0.00998, 0.0099811, 0.053371, 0.07458, 0.032661, 0.54502, 0.42801, 0.43888, 0.26576, 0.046506, 0.052943, 0.023176, 0.0099753, 0.0099753, 0.009975312, 0.052681, 0.072668, 0.032079, 0.57409, 0.42684, 0.44958, 0.27447, 0.046053, 0.052622, 0.022701, 0.0099702, 0.0099702, 0.009970213, 0.052236, 0.071905, 0.031348, 0.56773, 0.43854, 0.45804, 0.28024, 0.045605, 0.052407, 0.022196, 0.0099645, 0.0099645, 0.009964514, 0.052264, 0.072108, 0.030936, 0.57746, 0.4412, 0.46565, 0.28636, 0.045278, 0.052168, 0.02181, 0.0099584, 0.0099584, 0.009958415, 0.051566, 0.071671, 0.0305, 0.57607, 0.45039, 0.47213, 0.29103, 0.045031, 0.05199, 0.02146, 0.0099517, 0.0099517, 0.009951716, 0.051606, 0.071175, 0.030377, 0.58814, 0.44854, 0.47788, 0.29613, 0.044854, 0.051837, 0.021206, 0.0099446, 0.0099446, 0.009944617, 0.051317, 0.071775, 0.029695, 0.59891, 0.44795, 0.48104, 0.29932, 0.044679, 0.051704, 0.021012, 0.009937, 0.009937, 0.009937v5m
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr20, 0.088815, 0.083758, 0.083028, 0.10967, 0.060776, 0.0084766, 0.0028998, 0.07658, 0.065096, 0.075192, 0.0033297, 0.0033297, 0.0700321, 0.07153, 0.082675, 0.069671, 0.25921, 0.12556, 0.050926, 0.021388, 0.067456, 0.063615, 0.058973, 0.0066629, 0.0066629, 0.0400322, 0.065678, 0.079942, 0.056423, 0.28735, 0.19372, 0.13283, 0.059436, 0.060568, 0.061682, 0.046271, 0.0099954, 0.0099954, 0.0100313, 0.061338, 0.078504, 0.04723, 0.40063, 0.24958, 0.22877, 0.11608, 0.055906, 0.058371, 0.037091, 0.0099978, 0.0099978, 0.00999784, 0.058455, 0.076397, 0.042069, 0.42689, 0.30947, 0.28819, 0.15312, 0.053267, 0.057035, 0.032819, 0.0099978, 0.0099978, 0.00999785, 0.056665, 0.075084, 0.038737, 0.48373, 0.33178, 0.32958, 0.18127, 0.051449, 0.05598, 0.03003, 0.0099961, 0.0099961, 0.00999616, 0.055591, 0.075298, 0.036506, 0.49513, 0.36799, 0.36491, 0.2067, 0.049934, 0.054871, 0.027925, 0.0099938, 0.0099938, 0.00999387, 0.054502, 0.073199, 0.035426, 0.49643, 0.39808, 0.38942, 0.22583, 0.048767, 0.054358, 0.026238, 0.0099911, 0.0099911, 0.00999118, 0.053876, 0.07238, 0.033299, 0.52135, 0.41381, 0.41562, 0.24501, 0.04781, 0.053518, 0.024843, 0.0099879, 0.0099879, 0.00998799, 0.053236, 0.072389, 0.032481, 0.54612, 0.41269, 0.43074, 0.25796, 0.046989, 0.053086, 0.023894, 0.0099842, 0.0099842, 0.009984210, 0.052832, 0.07241, 0.031733, 0.55228, 0.43234, 0.44671, 0.26953, 0.046382, 0.052618, 0.023047, 0.00998, 0.00998, 0.0099811, 0.05282, 0.074112, 0.031097, 0.5533, 0.44669, 0.46024, 0.28065, 0.045857, 0.052293, 0.022248, 0.0099753, 0.0099753, 0.009975312, 0.052155, 0.071912, 0.030904, 0.55584, 0.45631, 0.46781, 0.28701, 0.045427, 0.051986, 0.021778, 0.0099702, 0.0099702, 0.009970213, 0.051747, 0.072218, 0.03014, 0.60159, 0.44424, 0.47684, 0.29523, 0.045071, 0.051834, 0.021251, 0.0099645, 0.0099645, 0.009964514, 0.051199, 0.071139, 0.029753, 0.60888, 0.44745, 0.4834, 0.30013, 0.044758, 0.051615, 0.020943, 0.0099584, 0.0099584, 0.009958415, 0.051116, 0.071044, 0.029682, 0.61669, 0.45095, 0.48788, 0.30411, 0.044482, 0.051461, 0.020708, 0.0099517, 0.0099517, 0.009951716, 0.051165, 0.071407, 0.029131, 0.58867, 0.46949, 0.49298, 0.30788, 0.044266, 0.051328, 0.020452, 0.0099446, 0.0099446, 0.009944617, 0.050888, 0.070982, 0.029018, 0.60247, 0.46829, 0.49698, 0.3109, 0.044097, 0.051225, 0.020252, 0.009937, 0.009937, 0.00993718, 0.050531, 0.070728, 0.028839, 0.62038, 0.46144, 0.49886, 0.31304, 0.04396, 0.051144, 0.020106, 0.0099289, 0.0099289, 0.009928919, 0.050503, 0.070549, 0.028389, 0.61684, 0.4655, 0.50101, 0.31475, 0.043849, 0.051041, 0.019979, 0.0099203, 0.0099203, 0.009920320, 0.050645, 0.070725, 0.028245, 0.60357, 0.47478, 0.5033, 0.31661, 0.043755, 0.050947, 0.019874, 0.0099112, 0.0099112, 0.009911221, 0.050031, 0.069934, 0.027804, 0.60496, 0.47581, 0.50527, 0.31852, 0.043667, 0.050866, 0.01977, 0.0099017, 0.0099017, 0.009901722, 0.049924, 0.069962, 0.027742, 0.61439, 0.47212, 0.50718, 0.32007, 0.043592, 0.050781, 0.019677, 0.0098916, 0.0098916, 0.009891623, 0.049774, 0.070259, 0.027356, 0.62347, 0.4707, 0.50993, 0.32225, 0.043526, 0.050693, 0.019594, 0.0098811, 0.0098811, 0.009881124, 0.049478, 0.06908, 0.0275, 0.59173, 0.49002, 0.51115, 0.32364, 0.043463, 0.050619, 0.019518, 0.0098701, 0.0098701, 0.009870125, 0.049521, 0.070211, 0.027425, 0.59696, 0.4898, 0.51278, 0.32486, 0.043407, 0.050557, 0.019451, 0.0098586, 0.0098586, 0.009858626, 0.049435, 0.07008, 0.027264, 0.61438, 0.48112, 0.51381, 0.32583, 0.043351, 0.050498, 0.019382, 0.0098467, 0.0098467, 0.009846727, 0.049354, 0.070545, 0.026766, 0.6064, 0.48604, 0.51499, 0.32726, 0.043301, 0.050441, 0.019321, 0.0098342, 0.0098342, 0.009834228, 0.049133, 0.069629, 0.02687, 0.63298, 0.47289, 0.51647, 0.32861, 0.043251, 0.050395, 0.019262, 0.0098213, 0.0098213, 0.009821329, 0.049244, 0.070239, 0.026565, 0.61425, 0.48472, 0.51749, 0.32981, 0.043202, 0.050362, 0.0192, 0.0098079, 0.0098079, 0.009807930, 0.049247, 0.069914, 0.026879, 0.62765, 0.47932, 0.51836, 0.33032, 0.043154, 0.050333, 0.019143, 0.0097941, 0.0097941, 0.009794131, 0.048997, 0.070243, 0.026534, 0.64588, 0.47083, 0.51933, 0.33128, 0.04311, 0.05031, 0.019085, 0.0097798, 0.0097798, 0.009779832, 0.048746, 0.06989, 0.026604, 0.62604, 0.48261, 0.52032, 0.3324, 0.043065, 0.050294, 0.019024, 0.009765, 0.009765, 0.00976533, 0.048844, 0.069789, 0.026362, 0.60369, 0.49778, 0.52143, 0.3331, 0.04302, 0.050284, 0.018967, 0.0097497, 0.0097497, 0.009749734, 0.048838, 0.06909, 0.026239, 0.6214, 0.48986, 0.52276, 0.33407, 0.042976, 0.050265, 0.018912, 0.009734, 0.009734, 0.00973435, 0.048356, 0.069284, 0.025822, 0.61233, 0.49574, 0.52344, 0.33489, 0.042929, 0.050244, 0.018861, 0.0097178, 0.0097178, 0.009717836, 0.048366, 0.068073, 0.025951, 0.61853, 0.49364, 0.5246, 0.3363, 0.042877, 0.050227, 0.018803, 0.0097011, 0.0097011, 0.009701137, 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