How to train

[markpanizales]

I have followed your instructions up to the training part, however whenever I execute the train command I got the following results.

mark@mark-G11CD:/media/mark/Data_Application/darknetFaceID$ ./darknet detector train cfg/face.data cfg/face.cfg darknet19_448.conv.23 face layer filters size input output 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32 2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64 4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128 8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256 12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512 18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024 24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024 25 route 16 26 reorg / 2 26 x 26 x 512 -> 13 x 13 x2048 27 route 26 24 28 conv 1024 3 x 3 / 1 13 x 13 x3072 -> 13 x 13 x1024 29 conv 30 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 30 30 detection Loading weights from darknet19_448.conv.23...Done! Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005 Loaded: 0.030896 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.438415, Avg Recall: -nan, count: 0 1: 9.846994, 9.846994 avg, 0.000100 rate, 0.156046 seconds, 1 images Loaded: 0.000030 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.388885, Avg Recall: -nan, count: 0 2: 7.840993, 9.646395 avg, 0.000100 rate, 0.101668 seconds, 2 images Loaded: 0.000020 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.304338, Avg Recall: -nan, count: 0 3: 4.401259, 9.121881 avg, 0.000100 rate, 0.101112 seconds, 3 images Loaded: 0.000020 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.214405, Avg Recall: -nan, count: 0 4: 1.664008, 8.376094 avg, 0.000100 rate, 0.090299 seconds, 4 images Loaded: 0.000022 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.146090, Avg Recall: -nan, count: 0 5: 0.564317, 7.594916 avg, 0.000100 rate, 0.095788 seconds, 5 images Loaded: 0.000020 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.091963, Avg Recall: -nan, count: 0 6: 0.164433, 6.851868 avg, 0.000100 rate, 0.094488 seconds, 6 images Loaded: 0.000020 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.062839, Avg Recall: -nan, count: 0 7: 0.074724, 6.174154 avg, 0.000100 rate, 0.089121 seconds, 7 images Loaded: 0.000020 seconds Region Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: 0.041137, Avg Recall: -nan, count: 0 8: 0.054264, 5.562165 avg, 0.000100 rate, 0.094170 seconds, 8 images Loaded: 0.000022 seconds

I only have 1 class which is me and below is my face.cfg file I used.

`[net]
batch=1
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
max_batches = 120000
policy=steps
steps=-1,100,80000,100000
scales=.1,10,.1,.1

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

#######

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[route]
layers=-9

[reorg]
stride=2

[route]
layers=-1,-3

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear

[region]
anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1

object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1

absolute=1
thresh = .6
random=0`