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YOLO8x_DeepPBC_Dataset

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  1. F1_curve.png +0 -0
  2. PR_curve.png +0 -0
  3. P_curve.png +0 -0
  4. R_curve.png +0 -0
  5. confusion_matrix.png +0 -0
  6. confusion_matrix_normalized.png +0 -0
  7. labels.jpg +0 -0
  8. labels_correlogram.jpg +0 -0
  9. results.csv +51 -0
  10. results.png +0 -0
  11. train_batch0.jpg +0 -0
  12. train_batch1.jpg +0 -0
  13. train_batch15760.jpg +0 -0
  14. train_batch15761.jpg +0 -0
  15. train_batch15762.jpg +0 -0
  16. train_batch2.jpg +0 -0
  17. val_batch0_labels.jpg +0 -0
  18. val_batch0_pred.jpg +0 -0
  19. val_batch1_labels.jpg +0 -0
  20. val_batch1_pred.jpg +0 -0
  21. val_batch2_labels.jpg +0 -0
  22. val_batch2_pred.jpg +0 -0
F1_curve.png ADDED
PR_curve.png ADDED
P_curve.png ADDED
R_curve.png ADDED
confusion_matrix.png ADDED
confusion_matrix_normalized.png ADDED
labels.jpg ADDED
labels_correlogram.jpg ADDED
results.csv ADDED
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1
+ epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
2
+ 1, 2.1578, 1.7385, 1.5639, 0.61328, 0.51462, 0.51996, 0.1482, 2.6335, 1.4913, 1.674, 0.00033249, 0.00033249, 0.00033249
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+ 2, 1.9289, 1.2597, 1.4124, 0.6953, 0.70958, 0.73795, 0.39462, 1.6191, 1.3256, 1.273, 0.00065264, 0.00065264, 0.00065264
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+ 3, 1.913, 1.2327, 1.405, 0.89201, 0.86275, 0.92744, 0.51915, 1.4334, 0.82046, 1.1773, 0.00095959, 0.00095959, 0.00095959
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+ 4, 1.8639, 1.1793, 1.3854, 0.89844, 0.86632, 0.92675, 0.51071, 1.4906, 0.77602, 1.1719, 0.0009406, 0.0009406, 0.0009406
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+ 5, 1.8155, 1.151, 1.3485, 0.8771, 0.82205, 0.88246, 0.35768, 1.9125, 0.95648, 1.3196, 0.0009406, 0.0009406, 0.0009406
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+ 6, 1.742, 1.1085, 1.3166, 0.92708, 0.89102, 0.94259, 0.48944, 1.6537, 0.72884, 1.2326, 0.0009208, 0.0009208, 0.0009208
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+ 7, 1.725, 1.07, 1.3099, 0.85862, 0.82384, 0.88075, 0.33355, 2.1442, 0.88755, 1.4125, 0.000901, 0.000901, 0.000901
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+ 8, 1.7391, 1.0786, 1.3258, 0.94194, 0.89973, 0.96111, 0.58226, 1.3114, 0.65233, 1.1159, 0.0008812, 0.0008812, 0.0008812
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+ 9, 1.6754, 1.0575, 1.2807, 0.85064, 0.84361, 0.8511, 0.27132, 2.3833, 0.8791, 1.5667, 0.0008614, 0.0008614, 0.0008614
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+ 10, 1.6531, 1.06, 1.286, 0.85015, 0.85038, 0.84942, 0.23859, 2.575, 0.94975, 1.7196, 0.0008416, 0.0008416, 0.0008416
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+ 11, 1.6132, 1.0357, 1.2656, 0.87878, 0.86913, 0.90113, 0.33641, 2.1536, 0.84332, 1.4744, 0.0008218, 0.0008218, 0.0008218
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+ 12, 1.6226, 1.032, 1.263, 0.94869, 0.91597, 0.95615, 0.43935, 1.8732, 0.70149, 1.3291, 0.000802, 0.000802, 0.000802
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+ 13, 1.5826, 1.017, 1.2503, 0.93637, 0.91471, 0.96866, 0.6517, 1.1564, 0.57017, 1.0581, 0.0007822, 0.0007822, 0.0007822
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+ 14, 1.5713, 1.0179, 1.2473, 0.94783, 0.92557, 0.97127, 0.61224, 1.2812, 0.55681, 1.1056, 0.0007624, 0.0007624, 0.0007624
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+ 15, 1.525, 0.98955, 1.2176, 0.91639, 0.944, 0.96119, 0.44554, 1.8858, 0.68685, 1.3162, 0.0007426, 0.0007426, 0.0007426
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+ 16, 1.5373, 0.99301, 1.2229, 0.93814, 0.93213, 0.96003, 0.4709, 1.7568, 0.63751, 1.2743, 0.0007228, 0.0007228, 0.0007228
18
+ 17, 1.5025, 0.98144, 1.2073, 0.94662, 0.91954, 0.95735, 0.46011, 1.815, 0.65476, 1.3244, 0.000703, 0.000703, 0.000703
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+ 18, 1.5049, 0.97764, 1.2179, 0.9634, 0.93593, 0.97199, 0.51819, 1.7055, 0.6047, 1.2247, 0.0006832, 0.0006832, 0.0006832
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+ 19, 1.4557, 0.96094, 1.1921, 0.93549, 0.93524, 0.964, 0.45028, 1.8422, 0.69485, 1.2945, 0.0006634, 0.0006634, 0.0006634
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+ 20, 1.4619, 0.96764, 1.2062, 0.93579, 0.91874, 0.96007, 0.52332, 1.6519, 0.61787, 1.2352, 0.0006436, 0.0006436, 0.0006436
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+ 21, 1.449, 0.95626, 1.1849, 0.94533, 0.95795, 0.98238, 0.6997, 1.0267, 0.4525, 1.0185, 0.0006238, 0.0006238, 0.0006238
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+ 22, 1.4615, 0.9635, 1.1954, 0.95376, 0.94264, 0.97411, 0.58001, 1.3963, 0.54254, 1.1457, 0.000604, 0.000604, 0.000604
24
+ 23, 1.4363, 0.94383, 1.1879, 0.93234, 0.93857, 0.9688, 0.50694, 1.6793, 0.62337, 1.228, 0.0005842, 0.0005842, 0.0005842
25
+ 24, 1.4095, 0.92001, 1.1754, 0.94395, 0.94174, 0.96679, 0.52242, 1.6476, 0.58423, 1.2007, 0.0005644, 0.0005644, 0.0005644
26
+ 25, 1.4046, 0.91663, 1.1711, 0.94659, 0.95129, 0.97483, 0.52857, 1.6379, 0.56301, 1.2099, 0.0005446, 0.0005446, 0.0005446
27
+ 26, 1.3944, 0.91366, 1.1622, 0.96561, 0.94527, 0.98312, 0.63247, 1.2478, 0.4669, 1.0792, 0.0005248, 0.0005248, 0.0005248
28
+ 27, 1.3941, 0.906, 1.1689, 0.93688, 0.94897, 0.97139, 0.56266, 1.5095, 0.54397, 1.1808, 0.000505, 0.000505, 0.000505
29
+ 28, 1.3724, 0.88859, 1.1564, 0.95184, 0.94323, 0.96894, 0.53632, 1.6133, 0.55383, 1.2111, 0.0004852, 0.0004852, 0.0004852
30
+ 29, 1.3776, 0.8752, 1.1573, 0.94744, 0.93723, 0.95252, 0.3758, 2.1936, 0.67112, 1.4831, 0.0004654, 0.0004654, 0.0004654
31
+ 30, 1.3625, 0.86465, 1.1499, 0.95245, 0.93799, 0.96643, 0.44616, 1.9543, 0.64204, 1.3505, 0.0004456, 0.0004456, 0.0004456
32
+ 31, 1.3503, 0.8546, 1.1438, 0.95544, 0.94765, 0.97128, 0.4969, 1.7797, 0.59035, 1.27, 0.0004258, 0.0004258, 0.0004258
33
+ 32, 1.3408, 0.84873, 1.134, 0.95458, 0.93418, 0.97056, 0.52055, 1.6869, 0.56663, 1.2217, 0.000406, 0.000406, 0.000406
34
+ 33, 1.3447, 0.84816, 1.1347, 0.96154, 0.94473, 0.9743, 0.56296, 1.5483, 0.53315, 1.1746, 0.0003862, 0.0003862, 0.0003862
35
+ 34, 1.323, 0.82903, 1.1337, 0.95036, 0.946, 0.96947, 0.44398, 1.9881, 0.61521, 1.3575, 0.0003664, 0.0003664, 0.0003664
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+ 35, 1.3143, 0.82485, 1.1299, 0.95466, 0.94461, 0.97401, 0.52353, 1.7134, 0.55692, 1.2391, 0.0003466, 0.0003466, 0.0003466
37
+ 36, 1.308, 0.80277, 1.1335, 0.96211, 0.94031, 0.97617, 0.49306, 1.7935, 0.58727, 1.2646, 0.0003268, 0.0003268, 0.0003268
38
+ 37, 1.29, 0.79482, 1.1193, 0.96418, 0.95486, 0.97892, 0.53192, 1.6972, 0.54289, 1.2157, 0.000307, 0.000307, 0.000307
39
+ 38, 1.2926, 0.80328, 1.1258, 0.95405, 0.95176, 0.97719, 0.51893, 1.717, 0.56015, 1.2323, 0.0002872, 0.0002872, 0.0002872
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+ 39, 1.2749, 0.78091, 1.1173, 0.95872, 0.95048, 0.97268, 0.49433, 1.8228, 0.59518, 1.2878, 0.0002674, 0.0002674, 0.0002674
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+ 40, 1.2652, 0.77104, 1.1084, 0.97496, 0.94207, 0.98396, 0.58889, 1.5049, 0.51467, 1.1512, 0.0002476, 0.0002476, 0.0002476
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+ 41, 1.2275, 0.73941, 1.1648, 0.94974, 0.95732, 0.98073, 0.58242, 1.5026, 0.50933, 1.1541, 0.0002278, 0.0002278, 0.0002278
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+ 42, 1.1924, 0.72967, 1.1403, 0.95916, 0.95321, 0.9827, 0.59131, 1.4954, 0.503, 1.1497, 0.000208, 0.000208, 0.000208
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+ 43, 1.1862, 0.71294, 1.14, 0.9696, 0.95038, 0.98617, 0.59446, 1.5204, 0.49463, 1.1457, 0.0001882, 0.0001882, 0.0001882
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+ 44, 1.1656, 0.70251, 1.1285, 0.95913, 0.94909, 0.97983, 0.54493, 1.6971, 0.54457, 1.2379, 0.0001684, 0.0001684, 0.0001684
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+ 45, 1.1444, 0.69609, 1.1223, 0.97295, 0.94585, 0.98054, 0.56813, 1.6115, 0.546, 1.1965, 0.0001486, 0.0001486, 0.0001486
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+ 46, 1.1403, 0.6882, 1.1241, 0.96523, 0.95096, 0.98312, 0.58147, 1.5603, 0.51507, 1.1751, 0.0001288, 0.0001288, 0.0001288
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+ 47, 1.1291, 0.6786, 1.1184, 0.95785, 0.95996, 0.98371, 0.61339, 1.4518, 0.49735, 1.1369, 0.000109, 0.000109, 0.000109
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+ 48, 1.1142, 0.66991, 1.1095, 0.97403, 0.94437, 0.98645, 0.6234, 1.4186, 0.47958, 1.1205, 8.92e-05, 8.92e-05, 8.92e-05
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+ 49, 1.1138, 0.66855, 1.1105, 0.95282, 0.97163, 0.98616, 0.62315, 1.429, 0.47516, 1.1248, 6.94e-05, 6.94e-05, 6.94e-05
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+ 50, 1.0883, 0.65596, 1.0997, 0.96962, 0.95973, 0.98733, 0.63437, 1.3975, 0.46681, 1.113, 4.96e-05, 4.96e-05, 4.96e-05
results.png ADDED
train_batch0.jpg ADDED
train_batch1.jpg ADDED
train_batch15760.jpg ADDED
train_batch15761.jpg ADDED
train_batch15762.jpg ADDED
train_batch2.jpg ADDED
val_batch0_labels.jpg ADDED
val_batch0_pred.jpg ADDED
val_batch1_labels.jpg ADDED
val_batch1_pred.jpg ADDED
val_batch2_labels.jpg ADDED
val_batch2_pred.jpg ADDED