xhuvom
How to train
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`
