阈值比较

实验报告

前言

• 目的：挑选一个好阈值
• 方法：对比precision和recall, 权衡一下选一个差不多的，然后看看视频测试效果

yolov4-tiny-head-608×608_bushHead_v1_best.weights

• 阈值选择：0.3
• 等价于v5_101
• 0.30, precision = 0.75, recall = 0.65

class_id = 0, name = head, ap = 65.08%       (TP = 1231, FP = 2697) 
 for conf_thresh = 0.05, precision = 0.31, recall = 0.71, F1-score = 0.44 
 for conf_thresh = 0.05, TP = 1231, FP = 2697, FN = 495, average IoU = 22.76 % 
 for conf_thresh = 0.10, precision = 0.49, recall = 0.70, F1-score = 0.57 
 for conf_thresh = 0.10, TP = 1202, FP = 1262, FN = 524, average IoU = 35.48 % 
 for conf_thresh = 0.15, precision = 0.59, recall = 0.68, F1-score = 0.63 
 for conf_thresh = 0.15, TP = 1174, FP = 810, FN = 552, average IoU = 43.08 % 
 for conf_thresh = 0.20, precision = 0.65, recall = 0.67, F1-score = 0.66 
 for conf_thresh = 0.20, TP = 1155, FP = 610, FN = 571, average IoU = 47.71 % 
 for conf_thresh = 0.25, precision = 0.71, recall = 0.66, F1-score = 0.68 
 for conf_thresh = 0.25, TP = 1135, FP = 473, FN = 591, average IoU = 51.56 % 
 for conf_thresh = 0.30, precision = 0.75, recall = 0.65, F1-score = 0.69 
 for conf_thresh = 0.30, TP = 1119, FP = 378, FN = 607, average IoU = 54.68 % 
 for conf_thresh = 0.35, precision = 0.77, recall = 0.64, F1-score = 0.70 
 for conf_thresh = 0.35, TP = 1105, FP = 336, FN = 621, average IoU = 56.13 % 
 for conf_thresh = 0.40, precision = 0.80, recall = 0.63, F1-score = 0.70 
 for conf_thresh = 0.40, TP = 1085, FP = 279, FN = 641, average IoU = 58.26 % 
 for conf_thresh = 0.45, precision = 0.82, recall = 0.62, F1-score = 0.70 
 for conf_thresh = 0.45, TP = 1068, FP = 242, FN = 658, average IoU = 59.76 % 
 for conf_thresh = 0.50, precision = 0.83, recall = 0.60, F1-score = 0.70 
 for conf_thresh = 0.50, TP = 1038, FP = 214, FN = 688, average IoU = 60.91 % 
# WJ_testv1.1 大中小目标
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.272
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.651
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.175
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.209
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.446
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.437
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.220
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.371
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.377
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613

robot: yolov4-tiny-head-608×608_best.weights

• 阈值选择：0.3
• 0.15, precision = 0.58, recall = 0.64

class_id = 0, name = head, ap = 60.89%       (TP = 1134, FP = 1289)
for conf_thresh = 0.10, precision = 0.47, recall = 0.66, F1-score = 0.55
for conf_thresh = 0.10, TP = 1134, FP = 1289, FN = 592, average IoU = 34.33 %
for conf_thresh = 0.15, precision = 0.58, recall = 0.64, F1-score = 0.61
for conf_thresh = 0.15, TP = 1098, FP = 805, FN = 628, average IoU = 42.37 %
for conf_thresh = 0.20, precision = 0.64, recall = 0.62, F1-score = 0.63
for conf_thresh = 0.20, TP = 1074, FP = 602, FN = 652, average IoU = 47.15 %
for conf_thresh = 0.25, precision = 0.69, recall = 0.61, F1-score = 0.65
for conf_thresh = 0.25, TP = 1056, FP = 464, FN = 670, average IoU = 51.16 %
for conf_thresh = 0.30, precision = 0.73, recall = 0.60, F1-score = 0.66
for conf_thresh = 0.30, TP = 1037, FP = 388, FN = 689, average IoU = 53.67 %
for conf_thresh = 0.35, precision = 0.75, recall = 0.59, F1-score = 0.66
for conf_thresh = 0.35, TP = 1012, FP = 330, FN = 714, average IoU = 55.78 %
for conf_thresh = 0.40, precision = 0.78, recall = 0.57, F1-score = 0.66
for conf_thresh = 0.40, TP = 991, FP = 285, FN = 735, average IoU = 57.49 %
for conf_thresh = 0.45, precision = 0.79, recall = 0.56, F1-score = 0.66
for conf_thresh = 0.45, TP = 964, FP = 252, FN = 762, average IoU = 58.80 %
for conf_thresh = 0.50, precision = 0.81, recall = 0.54, F1-score = 0.65
for conf_thresh = 0.50, TP = 936, FP = 216, FN = 790, average IoU = 60.38 %
for conf_thresh = 0.55, precision = 0.82, recall = 0.52, F1-score = 0.64
for conf_thresh = 0.55, TP = 893, FP = 191, FN = 833, average IoU = 61.38 %
for conf_thresh = 0.60, precision = 0.84, recall = 0.50, F1-score = 0.63
for conf_thresh = 0.60, TP = 864, FP = 165, FN = 862, average IoU = 62.63 %

v4_51

• 阈值选择：0.35
• 0.20, precision = 0.62, recall = 0.65

class_id = 0, name = head, ap = 62.50%       (TP = 1199, FP = 3182)
for conf_thresh = 0.05, precision = 0.27, recall = 0.69, F1-score = 0.39
for conf_thresh = 0.05, TP = 1199, FP = 3182, FN = 527, average IoU = 19.81 %
for conf_thresh = 0.10, precision = 0.45, recall = 0.68, F1-score = 0.54
for conf_thresh = 0.10, TP = 1173, FP = 1443, FN = 553, average IoU = 32.52 %
for conf_thresh = 0.15, precision = 0.55, recall = 0.66, F1-score = 0.60
for conf_thresh = 0.15, TP = 1143, FP = 931, FN = 583, average IoU = 40.05 %
for conf_thresh = 0.20, precision = 0.62, recall = 0.65, F1-score = 0.64
for conf_thresh = 0.20, TP = 1120, FP = 673, FN = 606, average IoU = 45.48 %
for conf_thresh = 0.25, precision = 0.67, recall = 0.63, F1-score = 0.65
for conf_thresh = 0.25, TP = 1092, FP = 529, FN = 634, average IoU = 49.15 %
for conf_thresh = 0.30, precision = 0.70, recall = 0.62, F1-score = 0.66
for conf_thresh = 0.30, TP = 1063, FP = 447, FN = 663, average IoU = 51.49 %
for conf_thresh = 0.35, precision = 0.74, recall = 0.61, F1-score = 0.66
for conf_thresh = 0.35, TP = 1045, FP = 375, FN = 681, average IoU = 53.92 %
for conf_thresh = 0.40, precision = 0.77, recall = 0.59, F1-score = 0.67
for conf_thresh = 0.40, TP = 1022, FP = 309, FN = 704, average IoU = 56.32 %
for conf_thresh = 0.45, precision = 0.79, recall = 0.58, F1-score = 0.67
for conf_thresh = 0.45, TP = 1001, FP = 272, FN = 725, average IoU = 57.78 %
for conf_thresh = 0.50, precision = 0.81, recall = 0.56, F1-score = 0.66
for conf_thresh = 0.50, TP = 972, FP = 235, FN = 754, average IoU = 59.32 %
for conf_thresh = 0.55, precision = 0.82, recall = 0.55, F1-score = 0.66
for conf_thresh = 0.55, TP = 948, FP = 203, FN = 778, average IoU = 60.75 %
for conf_thresh = 0.60, precision = 0.85, recall = 0.53, F1-score = 0.65
for conf_thresh = 0.60, TP = 916, FP = 163, FN = 810, average IoU = 62.66 %

v4_101

• 阈值选择:0.3
• 0.20, precision = 0.63, recall = 0.65

class_id = 0, name = head, ap = 62.91%       (TP = 1222, FP = 2988)
for conf_thresh = 0.05, precision = 0.29, recall = 0.71, F1-score = 0.41
for conf_thresh = 0.05, TP = 1222, FP = 2988, FN = 504, average IoU = 21.10 %
for conf_thresh = 0.10, precision = 0.45, recall = 0.69, F1-score = 0.55
for conf_thresh = 0.10, TP = 1184, FP = 1420, FN = 542, average IoU = 33.15 %
for conf_thresh = 0.15, precision = 0.56, recall = 0.67, F1-score = 0.61
for conf_thresh = 0.15, TP = 1154, FP = 923, FN = 572, average IoU = 40.60 %
for conf_thresh = 0.20, precision = 0.63, recall = 0.65, F1-score = 0.64
for conf_thresh = 0.20, TP = 1128, FP = 676, FN = 598, average IoU = 45.71 %
for conf_thresh = 0.25, precision = 0.68, recall = 0.64, F1-score = 0.66
for conf_thresh = 0.25, TP = 1109, FP = 522, FN = 617, average IoU = 49.78 %
for conf_thresh = 0.30, precision = 0.72, recall = 0.63, F1-score = 0.67
for conf_thresh = 0.30, TP = 1080, FP = 430, FN = 646, average IoU = 52.52 %
for conf_thresh = 0.35, precision = 0.74, recall = 0.61, F1-score = 0.67
for conf_thresh = 0.35, TP = 1052, FP = 364, FN = 674, average IoU = 54.68 %
for conf_thresh = 0.40, precision = 0.77, recall = 0.60, F1-score = 0.67
for conf_thresh = 0.40, TP = 1031, FP = 310, FN = 695, average IoU = 56.69 %
for conf_thresh = 0.45, precision = 0.79, recall = 0.58, F1-score = 0.67
for conf_thresh = 0.45, TP = 1008, FP = 270, FN = 718, average IoU = 58.29 %
for conf_thresh = 0.50, precision = 0.81, recall = 0.57, F1-score = 0.67
for conf_thresh = 0.50, TP = 985, FP = 236, FN = 741, average IoU = 59.68 %
for conf_thresh = 0.55, precision = 0.83, recall = 0.56, F1-score = 0.67
for conf_thresh = 0.55, TP = 958, FP = 196, FN = 768, average IoU = 61.56 %
for conf_thresh = 0.60, precision = 0.85, recall = 0.54, F1-score = 0.66
for conf_thresh = 0.60, TP = 939, FP = 168, FN = 787, average IoU = 62.97 %

v5_51

• 阈值选择:0.3
• 0.30, precision = 0.72, recall = 0.65

class_id = 0, name = head, ap = 64.40%       (TP = 1232, FP = 2943)
for conf_thresh = 0.05, precision = 0.30, recall = 0.71, F1-score = 0.42
for conf_thresh = 0.05, TP = 1232, FP = 2943, FN = 494, average IoU = 21.46 %
for conf_thresh = 0.10, precision = 0.46, recall = 0.70, F1-score = 0.56
for conf_thresh = 0.10, TP = 1204, FP = 1389, FN = 522, average IoU = 33.79 %
for conf_thresh = 0.15, precision = 0.57, recall = 0.68, F1-score = 0.62
for conf_thresh = 0.15, TP = 1181, FP = 909, FN = 545, average IoU = 41.18 %
for conf_thresh = 0.20, precision = 0.63, recall = 0.67, F1-score = 0.65
for conf_thresh = 0.20, TP = 1163, FP = 677, FN = 563, average IoU = 46.12 %
for conf_thresh = 0.25, precision = 0.67, recall = 0.66, F1-score = 0.67
for conf_thresh = 0.25, TP = 1138, FP = 549, FN = 588, average IoU = 49.32 %
for conf_thresh = 0.30, precision = 0.72, recall = 0.65, F1-score = 0.68
for conf_thresh = 0.30, TP = 1119, FP = 440, FN = 607, average IoU = 52.54 %
for conf_thresh = 0.35, precision = 0.75, recall = 0.63, F1-score = 0.69
for conf_thresh = 0.35, TP = 1091, FP = 364, FN = 635, average IoU = 55.00 %
for conf_thresh = 0.40, precision = 0.78, recall = 0.62, F1-score = 0.69
for conf_thresh = 0.40, TP = 1073, FP = 305, FN = 653, average IoU = 57.14 %
for conf_thresh = 0.45, precision = 0.80, recall = 0.61, F1-score = 0.69
for conf_thresh = 0.45, TP = 1055, FP = 263, FN = 671, average IoU = 58.76 %
for conf_thresh = 0.50, precision = 0.82, recall = 0.60, F1-score = 0.69
for conf_thresh = 0.50, TP = 1036, FP = 222, FN = 690, average IoU = 60.45 %
for conf_thresh = 0.55, precision = 0.84, recall = 0.58, F1-score = 0.69
for conf_thresh = 0.55, TP = 1002, FP = 197, FN = 724, average IoU = 61.50 %
for conf_thresh = 0.60, precision = 0.85, recall = 0.56, F1-score = 0.68
for conf_thresh = 0.60, TP = 972, FP = 166, FN = 754, average IoU = 63.07 %

v5_31

• 阈值选择:0.25
• 0.25, precision = 0.70, recall = 0.65

class_id = 0, name = head, ap = 63.90%       (TP = 1225, FP = 2854)
for conf_thresh = 0.05, precision = 0.30, recall = 0.71, F1-score = 0.42
for conf_thresh = 0.05, TP = 1225, FP = 2854, FN = 501, average IoU = 21.92 %
for conf_thresh = 0.10, precision = 0.47, recall = 0.69, F1-score = 0.56
for conf_thresh = 0.10, TP = 1196, FP = 1353, FN = 530, average IoU = 34.33 %
for conf_thresh = 0.15, precision = 0.57, recall = 0.67, F1-score = 0.62
for conf_thresh = 0.15, TP = 1165, FP = 863, FN = 561, average IoU = 42.10 %
for conf_thresh = 0.20, precision = 0.65, recall = 0.66, F1-score = 0.65
for conf_thresh = 0.20, TP = 1145, FP = 628, FN = 581, average IoU = 47.39 %
for conf_thresh = 0.25, precision = 0.70, recall = 0.65, F1-score = 0.67
for conf_thresh = 0.25, TP = 1120, FP = 489, FN = 606, average IoU = 51.17 %
for conf_thresh = 0.30, precision = 0.73, recall = 0.64, F1-score = 0.68
for conf_thresh = 0.30, TP = 1102, FP = 414, FN = 624, average IoU = 53.51 %
for conf_thresh = 0.35, precision = 0.75, recall = 0.62, F1-score = 0.68
for conf_thresh = 0.35, TP = 1070, FP = 354, FN = 656, average IoU = 55.39 %
for conf_thresh = 0.40, precision = 0.78, recall = 0.61, F1-score = 0.68
for conf_thresh = 0.40, TP = 1050, FP = 297, FN = 676, average IoU = 57.55 %
for conf_thresh = 0.45, precision = 0.80, recall = 0.60, F1-score = 0.69
for conf_thresh = 0.45, TP = 1031, FP = 253, FN = 695, average IoU = 59.29 %
for conf_thresh = 0.50, precision = 0.82, recall = 0.58, F1-score = 0.68
for conf_thresh = 0.50, TP = 1004, FP = 222, FN = 722, average IoU = 60.61 %
for conf_thresh = 0.55, precision = 0.84, recall = 0.56, F1-score = 0.67
for conf_thresh = 0.55, TP = 973, FP = 192, FN = 753, average IoU = 61.89 %
for conf_thresh = 0.60, precision = 0.85, recall = 0.54, F1-score = 0.66
for conf_thresh = 0.60, TP = 932, FP = 168, FN = 794, average IoU = 62.88 %