本文为MTCNN训练中截取的关键信息.
数据集:
wider
[2016-11-30 21:20:48,380][INFO] total images, train: 12797, val: 3196
[2016-11-30 21:20:48,382][INFO] total faces, train: 97311, val: 24101
[2016-11-30 21:22:56,825][INFO] writes 216096 positives, 405760 negatives, 238143 part
celeba
[2016-11-30 21:23:08,400][INFO] total images, train: 162079, val: 40520
[2016-11-30 21:23:08,400][INFO] writing train data, 162079 images
[2016-11-30 21:31:17,145][INFO] writes 405000 landmark faces
网络:
pnet
耗时20分钟
test_iter: 1557
test_interval: 6278
base_lr: 0.05
display: 500
max_iter: 125560
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 31390
snapshot: 6278
Namespace(epoch=20, gpu=0, lr=0.05, lrp=5, lrw=0.1, net='p', size=128, snapshot=None)
I1130 21:31:22.283428 11185 solver.cpp:228] Iteration 0, loss = 0.593411
I1130 21:31:22.283479 11185 solver.cpp:244] Train net output #0: bbox_reg_loss = 0.0405373 (* 0.5 = 0.0202686 loss)
I1130 21:31:22.283486 11185 solver.cpp:244] Train net output #1: face_cls_loss = 0.366912 (* 1 = 0.366912 loss)
I1130 21:31:22.283490 11185 solver.cpp:244] Train net output #2: face_cls_neg_acc = 0.921875
I1130 21:31:22.283501 11185 solver.cpp:244] Train net output #3: face_cls_pos_acc = 0.0390625
I1130 21:31:22.283506 11185 solver.cpp:244] Train net output #4: landmark_reg_loss = 0.412462 (* 0.5 = 0.206231 loss)
I1130 21:42:35.476652 11185 solver.cpp:337] Iteration 69058, Testing net (#0)
I1130 21:42:47.591168 11185 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.0133936 (* 0.5 = 0.00669681 loss)
I1130 21:42:47.591210 11185 solver.cpp:404] Test net output #1: face_cls_loss = 0.106217 (* 1 = 0.106217 loss)
I1130 21:42:47.591215 11185 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.966099
I1130 21:42:47.591219 11185 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.816118
I1130 21:42:47.591225 11185 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.00465923 (* 0.5 = 0.00232961 loss)
I1130 21:51:50.719760 11185 solver.cpp:337] Iteration 125560, Testing net (#0)
I1130 21:52:02.954237 11185 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.0132568 (* 0.5 = 0.00662839 loss)
I1130 21:52:02.954282 11185 solver.cpp:404] Test net output #1: face_cls_loss = 0.105307 (* 1 = 0.105307 loss)
I1130 21:52:02.954288 11185 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.969522
I1130 21:52:02.954290 11185 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.807543
I1130 21:52:02.954295 11185 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.0046395 (* 0.5 = 0.00231975 loss)
rnet耗时40分钟
test_iter: 1679
test_interval: 6810
base_lr: 0.01
display: 500
max_iter: 272400
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 68100
snapshot: 6810
snapshot_prefix: "tmp/rnet"
solver_mode: GPU
net: "proto/r_train_val.prototxt"
test_initialization: false
average_loss: 500
Namespace(epoch=40, gpu=0, lr=0.01, lrp=10, lrw=0.1, net='r', size=64, snapshot=None)
I1130 22:09:35.275794 11845 solver.cpp:228] Iteration 0, loss = 0.987914
I1130 22:09:35.275873 11845 solver.cpp:244] Train net output #0: bbox_reg_loss = 0.0379053 (* 0.5 = 0.0189526 loss)
I1130 22:09:35.275892 11845 solver.cpp:244] Train net output #1: face_cls_loss = 0.390344 (* 1 = 0.390344 loss)
I1130 22:09:35.275903 11845 solver.cpp:244] Train net output #2: face_cls_neg_acc = 0.473958
I1130 22:09:35.275913 11845 solver.cpp:244] Train net output #3: face_cls_pos_acc = 0.6875
I1130 22:09:35.275925 11845 solver.cpp:244] Train net output #4: landmark_reg_loss = 0.578617 (* 1 = 0.578617 loss)
I1130 22:12:33.694891 11845 solver.cpp:337] Iteration 20430, Testing net (#0)
I1130 22:12:44.435087 11845 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.00908341 (* 0.5 = 0.00454171 loss)
I1130 22:12:44.435127 11845 solver.cpp:404] Test net output #1: face_cls_loss = 0.0609873 (* 1 = 0.0609873 loss)
I1130 22:12:44.435133 11845 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.99416
I1130 22:12:44.435137 11845 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.869091
I1130 22:12:44.435142 11845 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.00228393 (* 1 = 0.00228393 loss)
I1130 22:50:48.909255 11845 solver.cpp:337] Iteration 272400, Testing net (#0)
I1130 22:50:59.960093 11845 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.0081554 (* 0.5 = 0.0040777 loss)
I1130 22:50:59.960135 11845 solver.cpp:404] Test net output #1: face_cls_loss = 0.0447745 (* 1 = 0.0447745 loss)
I1130 22:50:59.960140 11845 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.993844
I1130 22:50:59.960144 11845 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.911061
I1130 22:50:59.960149 11845 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.00189443 (* 1 = 0.00189443 loss)
onet耗时100分钟
test_iter: 1042
test_interval: 4299
base_lr: 0.01
display: 500
max_iter: 171960
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 42990
snapshot: 4299
snapshot_prefix: "tmp/onet"
solver_mode: GPU
net: "proto/o_train_val.prototxt"
test_initialization: false
Namespace(epoch=40, gpu=0, lr=0.01, lrp=10, lrw=0.1, net='o', size=64, snapshot=None)
I1130 23:18:21.946321 12698 solver.cpp:228] Iteration 0, loss = 0.818923
I1130 23:18:21.946379 12698 solver.cpp:244] Train net output #0: bbox_reg_loss = 0.0412246 (* 0.5 = 0.0206123 loss)
I1130 23:18:21.946389 12698 solver.cpp:244] Train net output #1: face_cls_loss = 0.342851 (* 1 = 0.342851 loss)
I1130 23:18:21.946395 12698 solver.cpp:244] Train net output #2: face_cls_neg_acc = 0.664062
I1130 23:18:21.946401 12698 solver.cpp:244] Train net output #3: face_cls_pos_acc = 0.421875
I1130 23:18:21.946408 12698 solver.cpp:244] Train net output #4: landmark_reg_loss = 0.45546 (* 1 = 0.45546 loss)
I1130 23:37:50.175259 12698 solver.cpp:337] Iteration 34392, Testing net (#0)
I1130 23:38:15.145500 12698 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.00609372 (* 0.5 = 0.00304686 loss)
I1130 23:38:15.145556 12698 solver.cpp:404] Test net output #1: face_cls_loss = 0.0667225 (* 1 = 0.0667225 loss)
I1130 23:38:15.145562 12698 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.967708
I1130 23:38:15.145567 12698 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.931667
I1130 23:38:15.145572 12698 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.00156244 (* 1 = 0.00156244 loss)
I1201 00:57:01.713709 12698 solver.cpp:337] Iteration 171960, Testing net (#0)
I1201 00:57:27.383432 12698 solver.cpp:404] Test net output #0: bbox_reg_loss = 0.00565168 (* 0.5 = 0.00282584 loss)
I1201 00:57:27.383491 12698 solver.cpp:404] Test net output #1: face_cls_loss = 0.0569045 (* 1 = 0.0569045 loss)
I1201 00:57:27.383497 12698 solver.cpp:404] Test net output #2: face_cls_neg_acc = 0.976772
I1201 00:57:27.383502 12698 solver.cpp:404] Test net output #3: face_cls_pos_acc = 0.937635
I1201 00:57:27.383507 12698 solver.cpp:404] Test net output #4: landmark_reg_loss = 0.00137772 (* 1 = 0.00137772 loss)