YOLO_weight_extractor support one class cfg and .weights file?

[123liluky]

I used one class cfg and .weights file like this: ./darknet yolo test cfg/yolo1-tiny-obj.cfg yolo1-tiny-obj_40000.weights. Then error happened in [detection] layer: 15: darknet: ./src/detection_layer.c:25: make_detection_layer: Assertion `sideside((1 + l.coords)*l.n + l.classes) == inputs' failed. 已放弃 (核心已转储) My cfg file like this: [net] batch=64 subdivisions=8 height=448 width=448 channels=3 momentum=0.9 decay=0.0005

saturation=.75 exposure=.75 hue = .1

learning_rate=0.0005 policy=steps steps=200,400,600,800,20000,30000 scales=2.5,2,2,2,.1,.1 max_batches = 40000

[convolutional] batch_normalize=1 filters=16 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[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

[maxpool] size=2 stride=2

[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

[maxpool] size=2 stride=2

[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=256 activation=leaky

[connected] output= 1470 activation=linear

[detection] classes=1 coords=4 rescore=1 side=7 num=2 softmax=0 sqrt=1 jitter=.2

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

Have anyone known how to solve the problem? Thank you.

[123liluky]

I solved it. It means output in last [connected] layer should be equal to sideside((1 + l.coords)l.n + l.classes)