keras-yolo3: retrain model with VOC dataset, but can't find box in yolo.py

Thank you for this great model.

I’m new in yolo, I retrained this model with VOC dataset and pretrained darknet53_weights. After 30 epoches, I got a trained_weights.h5 with about 39.0 loss. In my training, I modified config in train.py to auto save model in h5, as follow:

    checkpoint = ModelCheckpoint(log_dir + "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5",
        monitor='val_loss', save_weights_only=False, save_best_only=True)

So I just modified model_path and classes_path in yolo.py. But using python yolo.py, my model can’t find any box in my test car picture(yolo.h5 can find cars)

...
model_data/trained_weights.h5 model, anchors, and classes loaded.
Input image filename:/home/cooli7wa/Desktop/car/1.jpg
(416, 416, 3)
Found 0 boxes for img
2.1818076580530033

After this, I modified score from 0.3 to 0.0, to see all boxes.

Found 400 boxes for img
tvmonitor 0.24 (1078, 716) (1440, 1030)
tvmonitor 0.24 (854, 52) (1249, 365)
tvmonitor 0.24 (565, 0) (1096, 441)
tvmonitor 0.24 (1010, 411) (1440, 1080)
tvmonitor 0.24 (300, 164) (696, 475)
tvmonitor 0.24 (233, 0) (763, 441)
tvmonitor 0.24 (743, 605) (1139, 919)
tvmonitor 0.24 (632, 716) (1029, 1029)
tvmonitor 0.24 (856, 717) (1248, 1030)
tvmonitor 0.24 (852, 937) (1251, 1080)
tvmonitor 0.24 (187, 938) (587, 1080)
tvmonitor 0.24 (564, 524) (1097, 1080)
tvmonitor 0.24 (1075, 939) (1440, 1080)
tvmonitor 0.24 (299, 272) (696, 587)
tvmonitor 0.24 (411, 52) (806, 365)
tvmonitor 0.24 (522, 162) (918, 476)
tvmonitor 0.24 (744, 495) (1138, 808)
tvmonitor 0.24 (630, 938) (1030, 1080)
tvmonitor 0.25 (190, 53) (584, 365)
tvmonitor 0.26 (79, 162) (473, 477)
train 0.24 (854, 52) (1249, 365)
train 0.24 (564, 415) (1097, 1080)
train 0.24 (79, 275) (472, 586)
train 0.24 (852, 828) (1251, 1080)
train 0.24 (786, 0) (1318, 666)
train 0.24 (343, 415) (875, 1080)
train 0.24 (787, 529) (1317, 1080)
train 0.24 (119, 750) (655, 1080)
train 0.24 (522, 162) (918, 476)
train 0.24 (854, 605) (1248, 920)
train 0.24 (563, 749) (1099, 1080)
train 0.24 (856, 717) (1248, 1030)
train 0.24 (79, 162) (473, 477)
train 0.24 (455, 195) (984, 886)
train 0.24 (342, 0) (875, 554)
train 0.24 (80, 494) (471, 808)
train 0.24 (854, 163) (1250, 476)
train 0.24 (0, 83) (431, 776)
train 0.24 (676, 303) (1206, 998)
train 0.25 (14, 417) (539, 1080)
sofa 0.25 (521, 606) (918, 919)
sofa 0.25 (565, 0) (1096, 441)
sofa 0.25 (0, 163) (360, 477)
sofa 0.25 (232, 195) (764, 884)
sofa 0.25 (565, 304) (1095, 998)
sofa 0.25 (233, 0) (763, 441)
sofa 0.25 (0, 0) (319, 671)
sofa 0.25 (1011, 524) (1440, 1080)
sofa 0.25 (676, 194) (1206, 886)
sofa 0.25 (898, 0) (1427, 554)
sofa 0.25 (232, 639) (765, 1080)
sofa 0.25 (523, 826) (915, 1080)
sofa 0.25 (342, 0) (875, 554)
sofa 0.25 (455, 195) (984, 886)
sofa 0.25 (452, 640) (987, 1080)
sofa 0.25 (9, 644) (544, 1080)
sofa 0.25 (0, 0) (429, 445)
sofa 0.25 (784, 642) (1321, 1080)
sofa 0.26 (120, 0) (655, 549)
sofa 0.26 (78, 826) (474, 1080)
sheep 0.25 (521, 383) (918, 699)
sheep 0.25 (898, 0) (1427, 554)
sheep 0.25 (300, 383) (695, 698)
sheep 0.25 (522, 162) (918, 476)
sheep 0.25 (852, 828) (1251, 1080)
sheep 0.25 (521, 273) (918, 587)
sheep 0.25 (0, 749) (429, 1080)
sheep 0.25 (523, 495) (916, 809)
sheep 0.26 (455, 195) (984, 886)
sheep 0.26 (301, 717) (696, 1029)
sheep 0.26 (963, 162) (1362, 476)
sheep 0.26 (0, 0) (430, 667)
sheep 0.26 (744, 495) (1138, 808)
sheep 0.26 (854, 605) (1248, 920)
sheep 0.26 (299, 272) (696, 587)
sheep 0.26 (140, 350) (246, 563)
sheep 0.26 (740, 938) (1142, 1080)
sheep 0.26 (232, 195) (764, 884)
sheep 0.26 (0, 163) (360, 477)
sheep 0.26 (521, 606) (918, 919)
pottedplant 0.24 (786, 82) (1317, 777)
pottedplant 0.24 (1076, 606) (1440, 919)
pottedplant 0.24 (233, 0) (763, 441)
pottedplant 0.24 (675, 0) (1208, 554)
pottedplant 0.24 (676, 303) (1206, 998)
pottedplant 0.24 (13, 530) (540, 1080)
pottedplant 0.24 (452, 415) (987, 1080)
pottedplant 0.24 (12, 0) (540, 440)
pottedplant 0.24 (1010, 411) (1440, 1080)
pottedplant 0.24 (898, 0) (1428, 666)
pottedplant 0.24 (229, 84) (767, 774)
pottedplant 0.24 (0, 637) (427, 1080)
pottedplant 0.24 (564, 524) (1097, 1080)
pottedplant 0.24 (743, 605) (1139, 919)
pottedplant 0.24 (0, 0) (429, 330)
pottedplant 0.24 (565, 194) (1095, 886)
pottedplant 0.24 (0, 83) (431, 776)
pottedplant 0.25 (124, 194) (652, 885)
pottedplant 0.25 (344, 192) (873, 888)
pottedplant 0.25 (235, 526) (762, 1080)
person 0.26 (343, 415) (875, 1080)
person 0.26 (343, 0) (874, 440)
person 0.26 (232, 305) (764, 996)
person 0.26 (966, 494) (1361, 809)
person 0.26 (522, 162) (918, 476)
person 0.26 (854, 0) (1249, 253)
person 0.26 (898, 0) (1427, 554)
person 0.26 (454, 0) (986, 555)
person 0.26 (565, 0) (1096, 441)
person 0.26 (1145, 188) (1348, 614)
person 0.26 (896, 303) (1429, 998)
person 0.26 (78, 826) (474, 1080)
person 0.26 (457, 303) (983, 999)
person 0.27 (121, 418) (653, 1080)
person 0.27 (675, 0) (1208, 554)
person 0.27 (120, 0) (655, 549)
person 0.27 (676, 194) (1206, 886)
person 0.27 (12, 0) (540, 440)
person 0.27 (232, 0) (764, 663)
person 0.27 (787, 0) (1317, 440)
motorbike 0.24 (898, 0) (1427, 554)
motorbike 0.24 (966, 606) (1360, 920)
motorbike 0.24 (854, 163) (1250, 476)
motorbike 0.24 (343, 415) (875, 1080)
motorbike 0.24 (521, 52) (918, 366)
motorbike 0.24 (676, 303) (1206, 998)
motorbike 0.24 (412, 495) (806, 809)
motorbike 0.24 (1008, 639) (1440, 1080)
motorbike 0.24 (854, 52) (1249, 365)
motorbike 0.25 (232, 305) (764, 996)
motorbike 0.25 (0, 526) (428, 1080)
motorbike 0.25 (1077, 53) (1440, 365)
motorbike 0.25 (121, 530) (653, 1080)
motorbike 0.25 (455, 195) (984, 886)
motorbike 0.25 (522, 162) (918, 476)
motorbike 0.25 (521, 606) (918, 919)
motorbike 0.25 (454, 0) (986, 555)
motorbike 0.25 (14, 417) (539, 1080)
motorbike 0.25 (12, 0) (540, 553)
motorbike 0.26 (0, 83) (431, 776)
horse 0.25 (411, 52) (806, 365)
horse 0.25 (1010, 411) (1440, 1080)
horse 0.25 (854, 163) (1250, 476)
horse 0.25 (854, 52) (1249, 365)
horse 0.25 (522, 162) (918, 476)
horse 0.25 (0, 939) (364, 1080)
horse 0.25 (854, 0) (1249, 253)
horse 0.25 (851, 273) (1251, 587)
horse 0.25 (232, 305) (764, 996)
horse 0.25 (521, 273) (918, 587)
horse 0.25 (675, 0) (1208, 554)
horse 0.25 (452, 415) (987, 1080)
horse 0.25 (523, 495) (916, 809)
horse 0.25 (521, 383) (918, 699)
horse 0.25 (675, 413) (1207, 1080)
horse 0.25 (786, 0) (1318, 666)
horse 0.25 (344, 192) (873, 888)
horse 0.26 (453, 0) (986, 666)
horse 0.26 (14, 417) (539, 1080)
horse 0.26 (565, 194) (1095, 886)
dog 0.26 (674, 749) (1209, 1080)
dog 0.26 (631, 383) (1030, 699)
dog 0.26 (963, 937) (1362, 1080)
dog 0.26 (742, 162) (1139, 477)
dog 0.26 (676, 194) (1206, 886)
dog 0.26 (409, 937) (809, 1080)
dog 0.27 (744, 495) (1138, 808)
dog 0.27 (786, 82) (1317, 777)
dog 0.27 (78, 52) (474, 366)
dog 0.27 (521, 606) (918, 919)
dog 0.27 (299, 53) (696, 364)
dog 0.27 (78, 826) (474, 1080)
dog 0.27 (0, 163) (360, 477)
dog 0.27 (10, 750) (543, 1080)
dog 0.27 (191, 164) (583, 476)
dog 0.27 (77, 939) (476, 1080)
dog 0.27 (341, 85) (876, 775)
dog 0.27 (740, 938) (1142, 1080)
dog 0.27 (522, 162) (918, 476)
dog 0.27 (299, 272) (696, 587)
diningtable 0.25 (563, 749) (1099, 1080)
diningtable 0.25 (742, 162) (1139, 477)
diningtable 0.25 (342, 0) (875, 554)
diningtable 0.25 (674, 82) (1208, 776)
diningtable 0.25 (10, 750) (543, 1080)
diningtable 0.25 (411, 826) (807, 1080)
diningtable 0.25 (409, 937) (809, 1080)
diningtable 0.25 (785, 0) (1319, 553)
diningtable 0.25 (300, 383) (695, 698)
diningtable 0.25 (411, 52) (806, 365)
diningtable 0.25 (854, 52) (1249, 365)
diningtable 0.25 (963, 162) (1362, 476)
diningtable 0.25 (521, 273) (918, 587)
diningtable 0.25 (301, 495) (695, 808)
diningtable 0.25 (630, 938) (1030, 1080)
diningtable 0.25 (232, 305) (764, 996)
diningtable 0.25 (521, 606) (918, 919)
diningtable 0.26 (522, 162) (918, 476)
diningtable 0.26 (77, 939) (476, 1080)
diningtable 0.26 (79, 162) (473, 477)
cow 0.25 (410, 716) (807, 1030)
cow 0.25 (565, 0) (1096, 667)
cow 0.25 (966, 494) (1361, 809)
cow 0.25 (852, 828) (1251, 1080)
cow 0.25 (411, 52) (806, 365)
cow 0.25 (676, 303) (1206, 998)
cow 0.25 (856, 717) (1248, 1030)
cow 0.25 (78, 52) (474, 366)
cow 0.25 (631, 383) (1030, 699)
cow 0.25 (1078, 716) (1440, 1030)
cow 0.25 (1076, 606) (1440, 919)
cow 0.26 (0, 0) (430, 667)
cow 0.26 (633, 162) (1027, 477)
cow 0.26 (1077, 53) (1440, 365)
cow 0.26 (854, 52) (1249, 365)
cow 0.26 (854, 163) (1250, 476)
cow 0.26 (898, 0) (1428, 666)
cow 0.26 (744, 495) (1138, 808)
cow 0.26 (854, 605) (1248, 920)
cow 0.26 (631, 607) (1029, 918)
chair 0.24 (632, 716) (1029, 1029)
chair 0.24 (411, 383) (806, 698)
chair 0.24 (301, 495) (695, 808)
chair 0.24 (854, 163) (1250, 476)
chair 0.24 (1075, 939) (1440, 1080)
chair 0.24 (1076, 606) (1440, 919)
chair 0.24 (411, 826) (807, 1080)
chair 0.25 (523, 495) (916, 809)
chair 0.25 (410, 716) (807, 1030)
chair 0.25 (744, 495) (1138, 808)
chair 0.25 (630, 938) (1030, 1080)
chair 0.25 (965, 53) (1361, 365)
chair 0.25 (745, 825) (1138, 1080)
chair 0.25 (411, 52) (806, 365)
chair 0.25 (852, 383) (1251, 698)
chair 0.25 (0, 939) (364, 1080)
chair 0.25 (854, 605) (1248, 920)
chair 0.25 (521, 606) (918, 919)
chair 0.25 (1011, 524) (1440, 1080)
chair 0.26 (966, 494) (1361, 809)
cat 0.25 (563, 749) (1099, 1080)
cat 0.25 (1077, 53) (1440, 365)
cat 0.25 (521, 383) (918, 699)
cat 0.25 (521, 0) (918, 141)
cat 0.25 (854, 605) (1248, 920)
cat 0.25 (520, 937) (919, 1080)
cat 0.25 (0, 0) (429, 445)
cat 0.25 (963, 937) (1362, 1080)
cat 0.25 (854, 52) (1249, 365)
cat 0.25 (410, 716) (807, 1030)
cat 0.25 (744, 495) (1138, 808)
cat 0.25 (522, 162) (918, 476)
cat 0.25 (521, 606) (918, 919)
cat 0.25 (852, 828) (1251, 1080)
cat 0.26 (190, 53) (584, 365)
cat 0.26 (632, 716) (1029, 1029)
cat 0.26 (740, 938) (1142, 1080)
cat 0.26 (77, 939) (476, 1080)
cat 0.26 (411, 52) (806, 365)
cat 0.26 (633, 827) (1027, 1080)
car 0.25 (565, 194) (1095, 886)
car 0.25 (232, 305) (764, 996)
car 0.25 (452, 415) (987, 1080)
car 0.26 (523, 495) (916, 809)
car 0.26 (785, 414) (1318, 1080)
car 0.26 (411, 52) (806, 365)
car 0.26 (966, 606) (1360, 920)
car 0.26 (299, 827) (697, 1080)
car 0.26 (965, 53) (1361, 365)
car 0.26 (301, 495) (695, 808)
car 0.26 (897, 191) (1428, 889)
car 0.26 (521, 606) (918, 919)
car 0.26 (80, 494) (471, 808)
car 0.26 (79, 162) (473, 477)
car 0.27 (124, 194) (652, 885)
car 0.27 (189, 275) (584, 585)
car 0.27 (76, 384) (476, 699)
car 0.28 (14, 417) (539, 1080)
car 0.28 (0, 83) (431, 776)
car 0.28 (0, 274) (364, 586)
bus 0.24 (521, 606) (918, 919)
bus 0.24 (189, 275) (584, 585)
bus 0.24 (565, 194) (1095, 886)
bus 0.24 (123, 0) (651, 442)
bus 0.24 (743, 605) (1139, 919)
bus 0.24 (189, 384) (585, 698)
bus 0.24 (854, 52) (1249, 365)
bus 0.24 (343, 415) (875, 1080)
bus 0.24 (676, 0) (1206, 666)
bus 0.24 (563, 749) (1099, 1080)
bus 0.24 (79, 162) (473, 477)
bus 0.25 (232, 305) (764, 996)
bus 0.25 (785, 0) (1319, 553)
bus 0.25 (785, 305) (1320, 997)
bus 0.25 (124, 194) (652, 885)
bus 0.25 (675, 413) (1207, 1080)
bus 0.25 (630, 938) (1030, 1080)
bus 0.25 (898, 412) (1427, 1080)
bus 0.25 (897, 80) (1428, 778)
bus 0.26 (13, 87) (539, 771)
bottle 0.25 (812, 298) (1016, 726)
bottle 0.25 (0, 0) (185, 173)
bottle 0.25 (591, 465) (795, 891)
bottle 0.25 (92, 413) (297, 832)
bottle 0.25 (916, 796) (1021, 1004)
bottle 0.25 (1090, 628) (1292, 1060)
bottle 0.25 (1138, 517) (1243, 730)
bottle 0.25 (370, 77) (572, 504)
bottle 0.25 (141, 793) (245, 1006)
bottle 0.25 (37, 298) (239, 725)
bottle 0.25 (1090, 357) (1294, 777)
bottle 0.25 (38, 133) (241, 558)
bottle 0.25 (758, 77) (960, 504)
bottle 0.25 (202, 356) (407, 779)
bottle 0.25 (258, 465) (463, 891)
bottle 0.26 (150, 469) (350, 887)
bottle 0.26 (1193, 463) (1298, 673)
bottle 0.26 (140, 350) (246, 563)
bottle 0.26 (204, 132) (406, 559)
bottle 0.27 (93, 247) (296, 666)
boat 0.23 (124, 194) (652, 885)
boat 0.23 (342, 0) (875, 554)
boat 0.23 (0, 0) (430, 667)
boat 0.23 (1117, 0) (1440, 668)
boat 0.23 (187, 497) (586, 806)
boat 0.23 (898, 412) (1427, 1080)
boat 0.23 (10, 0) (543, 329)
boat 0.23 (123, 0) (651, 442)
boat 0.23 (854, 163) (1250, 476)
boat 0.23 (1010, 0) (1440, 555)
boat 0.23 (674, 82) (1208, 776)
boat 0.23 (14, 417) (539, 1080)
boat 0.24 (785, 0) (1319, 553)
boat 0.24 (344, 192) (873, 888)
boat 0.24 (787, 192) (1317, 888)
boat 0.24 (1009, 191) (1440, 889)
boat 0.24 (565, 194) (1095, 886)
boat 0.24 (897, 80) (1428, 778)
boat 0.24 (1008, 0) (1440, 218)
boat 0.24 (13, 87) (539, 771)
bird 0.26 (0, 827) (363, 1080)
bird 0.26 (412, 495) (806, 809)
bird 0.26 (521, 52) (918, 366)
bird 0.26 (13, 0) (540, 663)
bird 0.26 (852, 828) (1251, 1080)
bird 0.26 (0, 274) (364, 586)
bird 0.26 (740, 938) (1142, 1080)
bird 0.26 (77, 939) (476, 1080)
bird 0.26 (0, 0) (362, 142)
bird 0.26 (0, 495) (362, 808)
bird 0.26 (521, 273) (918, 587)
bird 0.26 (411, 826) (807, 1080)
bird 0.26 (745, 52) (1138, 366)
bird 0.27 (631, 607) (1029, 918)
bird 0.27 (119, 87) (655, 771)
bird 0.27 (522, 162) (918, 476)
bird 0.27 (411, 604) (806, 920)
bird 0.27 (0, 382) (365, 699)
bird 0.27 (189, 275) (584, 585)
bird 0.28 (79, 162) (473, 477)
bicycle 0.25 (411, 272) (806, 587)
bicycle 0.25 (758, 77) (960, 504)
bicycle 0.25 (302, 605) (695, 918)
bicycle 0.25 (521, 606) (918, 919)
bicycle 0.25 (189, 827) (585, 1080)
bicycle 0.25 (740, 938) (1142, 1080)
bicycle 0.25 (78, 603) (473, 921)
bicycle 0.25 (411, 52) (806, 365)
bicycle 0.25 (631, 383) (1030, 699)
bicycle 0.25 (742, 162) (1139, 477)
bicycle 0.26 (0, 827) (363, 1080)
bicycle 0.26 (522, 162) (918, 476)
bicycle 0.26 (745, 52) (1138, 366)
bicycle 0.26 (523, 495) (916, 809)
bicycle 0.26 (0, 495) (362, 808)
bicycle 0.26 (744, 495) (1138, 808)
bicycle 0.26 (411, 383) (806, 698)
bicycle 0.26 (189, 384) (585, 698)
bicycle 0.26 (189, 275) (584, 585)
bicycle 0.26 (301, 495) (695, 808)
aeroplane 0.23 (631, 607) (1029, 918)
aeroplane 0.23 (1008, 639) (1440, 1080)
aeroplane 0.23 (784, 642) (1321, 1080)
aeroplane 0.23 (854, 0) (1250, 35)
aeroplane 0.23 (813, 630) (1016, 1058)
aeroplane 0.23 (13, 87) (539, 771)
aeroplane 0.23 (411, 52) (806, 365)
aeroplane 0.23 (342, 0) (875, 554)
aeroplane 0.24 (425, 576) (628, 1002)
aeroplane 0.24 (79, 0) (474, 34)
aeroplane 0.24 (1146, 741) (1347, 1080)
aeroplane 0.24 (0, 939) (364, 1080)
aeroplane 0.24 (0, 274) (364, 586)
aeroplane 0.24 (1090, 796) (1292, 1080)
aeroplane 0.24 (37, 191) (242, 612)
aeroplane 0.24 (813, 134) (1016, 558)
aeroplane 0.24 (79, 162) (473, 477)
aeroplane 0.24 (76, 384) (476, 699)
aeroplane 0.24 (189, 275) (584, 585)
aeroplane 0.25 (758, 77) (960, 504)

all class have almost the same score… Other, I found my trained_weights.h5’s size is not same as yolo.h5

-rw-rw-r--  1 cooli7wa cooli7wa 248491304 6月   4 15:33 trained_weights.h5
-rw-rw-r--  1 cooli7wa cooli7wa 248686632 6月   1 09:18 yolo.h5

Is that size ok? Or my loss too high(39.0)? I don’t know why.

This blocked me two days. Thank you for your help!