[net] # Testing batch=1 subdivisions=1 # Training #batch=64 #subdivisions=8 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 burn_in=1000 max_batches = 4000 policy=steps steps=3200,3600 scales=.1,.1 [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=1 [convolutional] batch_normalize=1 filters=1024 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] size=1 stride=1 pad=1 filters=21 activation=linear [yolo] mask = 3,4,5 anchors = 17, 27, 8,140, 16,124, 37, 56, 18,211, 32,218, 83,105, 66,288, 142,185 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1 [route] layers = -4 [convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky [upsample] stride=2 [route] layers = -1, 8 [convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky [convolutional] size=1 stride=1 pad=1 filters=21 activation=linear [yolo] mask = 0,1,2 anchors = 17,27, 9,136, 33,54, 18,160, 67,82, 32,226, 103,130, 68,289, 147,195 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1