Merge pull request #1 from AlexeyAB/master

upstream

build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg

@@ -0,0 +1,807 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=512
+height=512
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+max_epochs = 300
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 6,7,8
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 3,4,5
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+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, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 0,1,2
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+random=1

cfg/Gaussian_yolov3_BDD.cfg

@@ -0,0 +1,807 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=512
+height=512
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+max_epochs = 300
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+[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
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 6,7,8
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 3,4,5
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+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, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=57
+activation=linear
+
+
+[Gaussian_yolo]
+mask = 0,1,2
+anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+iou_thresh=0.213
+uc_normalizer=1.0
+cls_normalizer=1.0
+iou_normalizer=0.5
+iou_loss=giou
+scale_x_y=1.0
+random=1

include/darknet.h

@@ -280,6 +280,7 @@ struct layer {
     int random;
     float ignore_thresh;
     float truth_thresh;
+    float iou_thresh;
     float thresh;
     float focus;
     int classfix;
@@ -329,6 +330,7 @@ struct layer {
     float *weight_updates;
 
     float scale_x_y;
+    float uc_normalizer;
     float iou_normalizer;
     float cls_normalizer;
     IOU_LOSS iou_loss;

src/box.c

@@ -424,7 +424,7 @@ int nms_comparator_v3(const void *pa, const void *pb)
     detection b = *(detection *)pb;
     float diff = 0;
     if (b.sort_class >= 0) {
-        diff = a.prob[b.sort_class] - b.prob[b.sort_class];
+        diff = a.prob[b.sort_class] - b.prob[b.sort_class]; // there is already: prob = objectness*prob
     }
     else {
         diff = a.objectness - b.objectness;

src/classifier.c

@@ -1288,4 +1288,6 @@ void run_classifier(int argc, char **argv)
     else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
     else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
     else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
+
+    if (gpus && gpu_list && ngpus > 1) free(gpus);
 }

src/coco.c

@@ -226,6 +226,12 @@ void validate_coco(char *cfgfile, char *weightfile)
     fprintf(fp, "\n]\n");
     fclose(fp);
 
+    if (val) free(val);
+    if (val_resized) free(val_resized);
+    if (buf) free(buf);
+    if (buf_resized) free(buf_resized);
+    if (thr) free(thr);
+
     fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
 }
 
@@ -307,7 +313,9 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
         }
 
         fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
-        free(id);
+
+        if (fps) free(fps);
+        if (id) free(id);
         free_image(orig);
         free_image(sized);
     }

src/convolutional_layer.c

@@ -405,6 +405,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w,
     l.nweights = (c / groups) * n * size * size;
 
     if (l.share_layer) {
+        if (l.size != l.share_layer->size || l.nweights != l.share_layer->nweights || l.c != l.share_layer->c || l.n != l.share_layer->n) {
+            printf("Layer size, nweights, channels or filters don't match for the share_layer");
+            getchar();
+        }
+
         l.weights = l.share_layer->weights;
         l.weight_updates = l.share_layer->weight_updates;
 

src/detector.c

@@ -556,6 +556,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
     for (j = 0; j < classes; ++j) {
         if (fps) fclose(fps[j]);
     }
+    if (fps) free(fps);
     if (coco) {
 #ifdef WIN32
         fseek(fp, -3, SEEK_CUR);
@@ -563,8 +564,15 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
         fseek(fp, -2, SEEK_CUR);
 #endif
         fprintf(fp, "\n]\n");
-        fclose(fp);
     }
+    if (fp) fclose(fp);
+
+    if (val) free(val);
+    if (val_resized) free(val_resized);
+    if (thr) free(thr);
+    if (buf) free(buf);
+    if (buf_resized) free(buf_resized);
+
     fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start);
 }
 
@@ -793,6 +801,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
             }
             //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1
             if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+            //if (nms) do_nms_obj(dets, nboxes, l.classes, nms);
 
             char labelpath[4096];
             replace_image_to_label(path, labelpath);
@@ -1099,6 +1108,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa
     else {
         free_network(net);
     }
+    if (val) free(val);
+    if (val_resized) free(val_resized);
+    if (thr) free(thr);
+    if (buf) free(buf);
+    if (buf_resized) free(buf_resized);
 
     return mean_average_precision;
 }
@@ -1505,4 +1519,6 @@ void run_detector(int argc, char **argv)
         free_list(options);
     }
     else printf(" There isn't such command: %s", argv[2]);
+
+    if (gpus && gpu_list && ngpus > 1) free(gpus);
 }

src/gaussian_yolo_layer.c

@@ -81,7 +81,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m
     */
 #endif
 
-    fprintf(stderr, "Gaussian_yolo\n");
+    //fprintf(stderr, "Gaussian_yolo\n");
     srand(time(0));
 
     return l;
@@ -140,32 +140,48 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int
     return b;
 }
 
-float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride)
+float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate)
 {
     box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride);
-    float iou = box_iou(pred, truth);
 
-    float tx = (truth.x*lw - i);
-    float ty = (truth.y*lh - j);
-    float tw = log(truth.w*w / biases[2*n]);
-    float th = log(truth.h*h / biases[2*n + 1]);
+    float iou;
+    ious all_ious = { 0 };
+    all_ious.iou = box_iou(pred, truth);
+    all_ious.giou = box_giou(pred, truth);
+    if (pred.w == 0) { pred.w = 1.0; }
+    if (pred.h == 0) { pred.h = 1.0; }
 
     float sigma_const = 0.3;
     float epsi = pow(10,-9);
 
-    float in_exp_x = (tx - x[index + 0*stride])/x[index+1*stride];
+    float dx, dy, dw, dh;
+
+    iou = all_ious.iou;
+
+    float tx = (truth.x*lw - i);
+    float ty = (truth.y*lh - j);
+    float tw = log(truth.w*w / biases[2 * n]);
+    float th = log(truth.h*h / biases[2 * n + 1]);
+
+    dx = (tx - x[index + 0 * stride]);
+    dy = (ty - x[index + 2 * stride]);
+    dw = (tw - x[index + 4 * stride]);
+    dh = (th - x[index + 6 * stride]);
+
+    // Gaussian
+    float in_exp_x = dx / x[index+1*stride];
     float in_exp_x_2 = pow(in_exp_x, 2);
     float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const));
 
-    float in_exp_y = (ty - x[index + 2*stride])/x[index+3*stride];
+    float in_exp_y = dy / x[index+3*stride];
     float in_exp_y_2 = pow(in_exp_y, 2);
     float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const));
 
-    float in_exp_w = (tw - x[index + 4*stride])/x[index+5*stride];
+    float in_exp_w = dw / x[index+5*stride];
     float in_exp_w_2 = pow(in_exp_w, 2);
     float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const));
 
-    float in_exp_h = (th - x[index + 6*stride])/x[index+7*stride];
+    float in_exp_h = dh / x[index+7*stride];
     float in_exp_h_2 = pow(in_exp_h, 2);
     float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const));
 
@@ -174,18 +190,96 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind
     float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale;
     float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale;
 
-    delta[index + 0*stride] = temp_x * in_exp_x  * (1./x[index+1*stride]);
-    delta[index + 2*stride] = temp_y * in_exp_y  * (1./x[index+3*stride]);
-    delta[index + 4*stride] = temp_w * in_exp_w  * (1./x[index+5*stride]);
-    delta[index + 6*stride] = temp_h * in_exp_h  * (1./x[index+7*stride]);
+    if (!accumulate) {
+        delta[index + 0 * stride] = 0;
+        delta[index + 1 * stride] = 0;
+        delta[index + 2 * stride] = 0;
+        delta[index + 3 * stride] = 0;
+        delta[index + 4 * stride] = 0;
+        delta[index + 5 * stride] = 0;
+        delta[index + 6 * stride] = 0;
+        delta[index + 7 * stride] = 0;
+    }
 
-    delta[index + 1*stride] = temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const));
-    delta[index + 3*stride] = temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const));
-    delta[index + 5*stride] = temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const));
-    delta[index + 7*stride] = temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const));
+    float delta_x = temp_x * in_exp_x  * (1. / x[index + 1 * stride]);
+    float delta_y = temp_y * in_exp_y  * (1. / x[index + 3 * stride]);
+    float delta_w = temp_w * in_exp_w  * (1. / x[index + 5 * stride]);
+    float delta_h = temp_h * in_exp_h  * (1. / x[index + 7 * stride]);
+
+    float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const));
+    float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const));
+    float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const));
+    float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const));
+
+    if (iou_loss != MSE) {
+        // GIoU
+        iou = all_ious.giou;
+
+        // https://github.com/generalized-iou/g-darknet
+        // https://arxiv.org/abs/1902.09630v2
+        // https://giou.stanford.edu/
+        all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
+
+        // jacobian^t (transpose)
+        float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
+        float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
+        float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
+        float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
+
+        // predict exponential, apply gradient of e^delta_t ONLY for w,h
+        dw *= exp(x[index + 4 * stride]);
+        dh *= exp(x[index + 6 * stride]);
+
+        // normalize iou weight, for GIoU
+        dx *= iou_normalizer;
+        dy *= iou_normalizer;
+        dw *= iou_normalizer;
+        dh *= iou_normalizer;
+
+        delta_x = dx;
+        delta_y = dy;
+        delta_w = dw;
+        delta_h = dh;
+    }
+
+    // normalize Uncertainty weight
+    delta_ux *= uc_normalizer;
+    delta_uy *= uc_normalizer;
+    delta_uw *= uc_normalizer;
+    delta_uh *= uc_normalizer;
+
+    delta[index + 0 * stride] += delta_x;
+    delta[index + 2 * stride] += delta_y;
+    delta[index + 4 * stride] += delta_w;
+    delta[index + 6 * stride] += delta_h;
+
+    delta[index + 1 * stride] += delta_ux;
+    delta[index + 3 * stride] += delta_uy;
+    delta[index + 5 * stride] += delta_uw;
+    delta[index + 7 * stride] += delta_uh;
     return iou;
 }
 
+void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
+{
+
+    int classes_in_one_box = 0;
+    int c;
+    for (c = 0; c < classes; ++c) {
+        if (delta[class_index + stride*c] > 0) classes_in_one_box++;
+    }
+
+    if (classes_in_one_box > 0) {
+        delta[box_index + 0 * stride] /= classes_in_one_box;
+        delta[box_index + 1 * stride] /= classes_in_one_box;
+        delta[box_index + 2 * stride] /= classes_in_one_box;
+        delta[box_index + 3 * stride] /= classes_in_one_box;
+        delta[box_index + 4 * stride] /= classes_in_one_box;
+        delta[box_index + 5 * stride] /= classes_in_one_box;
+        delta[box_index + 6 * stride] /= classes_in_one_box;
+        delta[box_index + 7 * stride] /= classes_in_one_box;
+    }
+}
 
 void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat)
 {
@@ -201,6 +295,19 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class
     }
 }
 
+int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
+{
+    int j;
+    for (j = 0; j < classes; ++j) {
+        //float prob = objectness * output[class_index + stride*j];
+        float prob = output[class_index + stride*j];
+        if (prob > conf_thresh) {
+            return 1;
+        }
+    }
+    return 0;
+}
+
 static int entry_gaussian_index(layer l, int batch, int location, int entry)
 {
     int n =   location / (l.w*l.h);
@@ -254,12 +361,31 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
                 for (n = 0; n < l.n; ++n) {
                     int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                     box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h);
+                    float best_match_iou = 0;
+                    int best_match_t = 0;
                     float best_iou = 0;
                     int best_t = 0;
                     for(t = 0; t < l.max_boxes; ++t){
                         box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
+                        int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
+                        if (class_id >= l.classes) {
+                            printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1);
+                            printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id);
+                            getchar();
+                            continue; // if label contains class_id more than number of classes in the cfg-file
+                        }
                         if(!truth.x) break;
+
+                        int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
+                        int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
+                        float objectness = l.output[obj_index];
+                        int class_id_match = compare_gaussian_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f);
+
                         float iou = box_iou(pred, truth);
+                        if (iou > best_match_iou && class_id_match == 1) {
+                            best_match_iou = iou;
+                            best_match_t = t;
+                        }
                         if (iou > best_iou) {
                             best_iou = iou;
                             best_t = t;
@@ -267,19 +393,19 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
                     }
                     int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
                     avg_anyobj += l.output[obj_index];
-                    l.delta[obj_index] = 0 - l.output[obj_index];
-                    if (best_iou > l.ignore_thresh) {
+                    l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]);
+                    if (best_match_iou > l.ignore_thresh) {
                         l.delta[obj_index] = 0;
                     }
                     if (best_iou > l.truth_thresh) {
-                        l.delta[obj_index] = 1 - l.output[obj_index];
+                        l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
 
                         int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4];
                         if (l.map) class_id = l.map[class_id];
                         int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
                         delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0);
                         box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
-                        delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
+                        delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
                     }
                 }
             }
@@ -308,11 +434,11 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
             int mask_n = int_index(l.mask, best_n, l.n);
             if(mask_n >= 0){
                 int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
-                float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
+                float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
 
                 int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
                 avg_obj += l.output[obj_index];
-                l.delta[obj_index] = 1 - l.output[obj_index];
+                l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
 
                 int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
                 if (l.map) class_id = l.map[class_id];
@@ -325,10 +451,116 @@ void forward_gaussian_yolo_layer(const layer l, network_state state)
                 if(iou > .75) recall75 += 1;
                 avg_iou += iou;
             }
+
+
+            // iou_thresh
+            for (n = 0; n < l.total; ++n) {
+                int mask_n = int_index(l.mask, n, l.n);
+                if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) {
+                    box pred = { 0 };
+                    pred.w = l.biases[2 * n] / state.net.w;
+                    pred.h = l.biases[2 * n + 1] / state.net.h;
+                    float iou = box_iou(pred, truth_shift);
+                    // iou, n
+
+                    if (iou > l.iou_thresh) {
+                        int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
+                        float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1);
+
+                        int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
+                        avg_obj += l.output[obj_index];
+                        l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
+
+                        int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
+                        if (l.map) class_id = l.map[class_id];
+                        int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9);
+                        delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat);
+
+                        ++count;
+                        ++class_count;
+                        if (iou > .5) recall += 1;
+                        if (iou > .75) recall75 += 1;
+                        avg_iou += iou;
+                    }
+                }
+            }
+        }
+
+        // averages the deltas obtained by the function: delta_yolo_box()_accumulate
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                    int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
+                    const int stride = l.w*l.h;
+
+                    averages_gaussian_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
+                }
+            }
         }
     }
+
+
+    // calculate: Classification-loss, IoU-loss and Uncertainty-loss
+    const int stride = l.w*l.h;
+    float* classification_lost = (float *)calloc(l.batch * l.outputs, sizeof(float));
+    memcpy(classification_lost, l.delta, l.batch * l.outputs * sizeof(float));
+
+
+    for (b = 0; b < l.batch; ++b) {
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+
+                    classification_lost[box_index + 0 * stride] = 0;
+                    classification_lost[box_index + 1 * stride] = 0;
+                    classification_lost[box_index + 2 * stride] = 0;
+                    classification_lost[box_index + 3 * stride] = 0;
+                    classification_lost[box_index + 4 * stride] = 0;
+                    classification_lost[box_index + 5 * stride] = 0;
+                    classification_lost[box_index + 6 * stride] = 0;
+                    classification_lost[box_index + 7 * stride] = 0;
+                }
+            }
+        }
+    }
+    float class_loss = pow(mag_array(classification_lost, l.outputs * l.batch), 2);
+    free(classification_lost);
+
+
+    float* except_uncertainty_lost = (float *)calloc(l.batch * l.outputs, sizeof(float));
+    memcpy(except_uncertainty_lost, l.delta, l.batch * l.outputs * sizeof(float));
+    for (b = 0; b < l.batch; ++b) {
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                    except_uncertainty_lost[box_index + 4 * stride] = 0;
+                    except_uncertainty_lost[box_index + 5 * stride] = 0;
+                    except_uncertainty_lost[box_index + 6 * stride] = 0;
+                    except_uncertainty_lost[box_index + 7 * stride] = 0;
+                }
+            }
+        }
+    }
+    float except_uc_loss = pow(mag_array(except_uncertainty_lost, l.outputs * l.batch), 2);
+    free(except_uncertainty_lost);
+
     *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
-    printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d\n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count);
+
+    float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+    float uc_loss = loss - except_uc_loss;
+    float iou_loss = except_uc_loss - class_loss;
+
+    loss /= l.batch;
+    class_loss /= l.batch;
+    uc_loss /= l.batch;
+    iou_loss /= l.batch;
+
+    printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d, loss = %.2f, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f \n",
+        state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count,
+        loss, class_loss, iou_loss, uc_loss);
 }
 
 void backward_gaussian_yolo_layer(const layer l, network_state state)

src/gemm.c

@@ -324,7 +324,7 @@ void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb)
     unsigned int x, y;
     for (y = 0; y < 32; ++y) {
         for (x = 0; x < 32; ++x) {
-            if (A[y * lda] & (1 << x)) B[x * ldb] |= (uint32_t)1 << y;
+            if (A[y * lda] & ((uint32_t)1 << x)) B[x * ldb] |= (uint32_t)1 << y;
         }
     }
 }
@@ -636,48 +636,48 @@ void check_cpu_features(void) {
     //  Detect Features
     if (nIds >= 0x00000001) {
         cpuid(info, 0x00000001);
-        HW_MMX = (info[3] & ((int)1 << 23)) != 0;
-        HW_SSE = (info[3] & ((int)1 << 25)) != 0;
-        HW_SSE2 = (info[3] & ((int)1 << 26)) != 0;
-        HW_SSE3 = (info[2] & ((int)1 << 0)) != 0;
+        HW_MMX = (info[3] & ((uint32_t)1 << 23)) != 0;
+        HW_SSE = (info[3] & ((uint32_t)1 << 25)) != 0;
+        HW_SSE2 = (info[3] & ((uint32_t)1 << 26)) != 0;
+        HW_SSE3 = (info[2] & ((uint32_t)1 << 0)) != 0;
 
-        HW_SSSE3 = (info[2] & ((int)1 << 9)) != 0;
-        HW_SSE41 = (info[2] & ((int)1 << 19)) != 0;
-        HW_SSE42 = (info[2] & ((int)1 << 20)) != 0;
-        HW_AES = (info[2] & ((int)1 << 25)) != 0;
+        HW_SSSE3 = (info[2] & ((uint32_t)1 << 9)) != 0;
+        HW_SSE41 = (info[2] & ((uint32_t)1 << 19)) != 0;
+        HW_SSE42 = (info[2] & ((uint32_t)1 << 20)) != 0;
+        HW_AES = (info[2] & ((uint32_t)1 << 25)) != 0;
 
-        HW_AVX = (info[2] & ((int)1 << 28)) != 0;
-        HW_FMA3 = (info[2] & ((int)1 << 12)) != 0;
+        HW_AVX = (info[2] & ((uint32_t)1 << 28)) != 0;
+        HW_FMA3 = (info[2] & ((uint32_t)1 << 12)) != 0;
 
-        HW_RDRAND = (info[2] & ((int)1 << 30)) != 0;
+        HW_RDRAND = (info[2] & ((uint32_t)1 << 30)) != 0;
     }
     if (nIds >= 0x00000007) {
         cpuid(info, 0x00000007);
-        HW_AVX2 = (info[1] & ((int)1 << 5)) != 0;
-
-        HW_BMI1 = (info[1] & ((int)1 << 3)) != 0;
-        HW_BMI2 = (info[1] & ((int)1 << 8)) != 0;
-        HW_ADX = (info[1] & ((int)1 << 19)) != 0;
-        HW_SHA = (info[1] & ((int)1 << 29)) != 0;
-        HW_PREFETCHWT1 = (info[2] & ((int)1 << 0)) != 0;
-
-        HW_AVX512F = (info[1] & ((int)1 << 16)) != 0;
-        HW_AVX512CD = (info[1] & ((int)1 << 28)) != 0;
-        HW_AVX512PF = (info[1] & ((int)1 << 26)) != 0;
-        HW_AVX512ER = (info[1] & ((int)1 << 27)) != 0;
-        HW_AVX512VL = (info[1] & ((int)1 << 31)) != 0;
-        HW_AVX512BW = (info[1] & ((int)1 << 30)) != 0;
-        HW_AVX512DQ = (info[1] & ((int)1 << 17)) != 0;
-        HW_AVX512IFMA = (info[1] & ((int)1 << 21)) != 0;
-        HW_AVX512VBMI = (info[2] & ((int)1 << 1)) != 0;
+        HW_AVX2 = (info[1] & ((uint32_t)1 << 5)) != 0;
+
+        HW_BMI1 = (info[1] & ((uint32_t)1 << 3)) != 0;
+        HW_BMI2 = (info[1] & ((uint32_t)1 << 8)) != 0;
+        HW_ADX = (info[1] & ((uint32_t)1 << 19)) != 0;
+        HW_SHA = (info[1] & ((uint32_t)1 << 29)) != 0;
+        HW_PREFETCHWT1 = (info[2] & ((uint32_t)1 << 0)) != 0;
+
+        HW_AVX512F = (info[1] & ((uint32_t)1 << 16)) != 0;
+        HW_AVX512CD = (info[1] & ((uint32_t)1 << 28)) != 0;
+        HW_AVX512PF = (info[1] & ((uint32_t)1 << 26)) != 0;
+        HW_AVX512ER = (info[1] & ((uint32_t)1 << 27)) != 0;
+        HW_AVX512VL = (info[1] & ((uint32_t)1 << 31)) != 0;
+        HW_AVX512BW = (info[1] & ((uint32_t)1 << 30)) != 0;
+        HW_AVX512DQ = (info[1] & ((uint32_t)1 << 17)) != 0;
+        HW_AVX512IFMA = (info[1] & ((uint32_t)1 << 21)) != 0;
+        HW_AVX512VBMI = (info[2] & ((uint32_t)1 << 1)) != 0;
     }
     if (nExIds >= 0x80000001) {
         cpuid(info, 0x80000001);
-        HW_x64 = (info[3] & ((int)1 << 29)) != 0;
-        HW_ABM = (info[2] & ((int)1 << 5)) != 0;
-        HW_SSE4a = (info[2] & ((int)1 << 6)) != 0;
-        HW_FMA4 = (info[2] & ((int)1 << 16)) != 0;
-        HW_XOP = (info[2] & ((int)1 << 11)) != 0;
+        HW_x64 = (info[3] & ((uint32_t)1 << 29)) != 0;
+        HW_ABM = (info[2] & ((uint32_t)1 << 5)) != 0;
+        HW_SSE4a = (info[2] & ((uint32_t)1 << 6)) != 0;
+        HW_FMA4 = (info[2] & ((uint32_t)1 << 16)) != 0;
+        HW_XOP = (info[2] & ((uint32_t)1 << 11)) != 0;
     }
 }
 

src/go.c

@@ -47,6 +47,7 @@ moves load_go_moves(char *filename)
     printf("%d\n", count);
     m.n = count;
     m.data = (char**)realloc(m.data, count * sizeof(char*));
+    fclose(fp);
     return m;
 }
 

src/image_opencv.cpp

@@ -703,11 +703,12 @@ int set_capture_position_frame_cv(cap_cv *cap, int index)
 
 image get_image_from_stream_cpp(cap_cv *cap)
 {
-    cv::Mat *src = new cv::Mat();
+    cv::Mat *src = NULL;
     static int once = 1;
     if (once) {
         once = 0;
         do {
+            if (src) delete src;
             src = get_capture_frame_cv(cap);
             if (!src) return make_empty_image(0, 0, 0);
         } while (src->cols < 1 || src->rows < 1 || src->channels() < 1);
@@ -719,6 +720,7 @@ image get_image_from_stream_cpp(cap_cv *cap)
     if (!src) return make_empty_image(0, 0, 0);
     image im = mat_to_image(*src);
     rgbgr_image(im);
+    if (src) delete src;
     return im;
 }
 // ----------------------------------------

src/network.c

@@ -820,6 +820,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names,
     const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold
 
     char *send_buf = (char *)calloc(1024, sizeof(char));
+    if (!send_buf) return 0;
     if (filename) {
         sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename);
     }
@@ -837,6 +838,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names,
                 if (class_id != -1) strcat(send_buf, ", \n");
                 class_id = j;
                 char *buf = (char *)calloc(2048, sizeof(char));
+                if (!buf) return 0;
                 //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}",
                 //    image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);
 
@@ -847,7 +849,10 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names,
                 int buf_len = strlen(buf);
                 int total_len = send_buf_len + buf_len + 100;
                 send_buf = (char *)realloc(send_buf, total_len * sizeof(char));
-                if (!send_buf) return 0;// exit(-1);
+                if (!send_buf) {
+                    if (buf) free(buf);
+                    return 0;// exit(-1);
+                }
                 strcat(send_buf, buf);
                 free(buf);
             }

src/parser.c

@@ -380,6 +380,7 @@ layer parse_yolo(list *options, size_params params)
 
     l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
     l.truth_thresh = option_find_float(options, "truth_thresh", 1);
+    l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
     l.random = option_find_int_quiet(options, "random", 0);
 
     char *map_file = option_find_str(options, "map", 0);
@@ -435,14 +436,29 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3
     char *a = option_find_str(options, "mask", 0);
     int *mask = parse_gaussian_yolo_mask(a, &num);
     layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
-    assert(l.outputs == params.inputs);
+    if (l.outputs != params.inputs) {
+        printf("Error: l.outputs == params.inputs \n");
+        printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n");
+        exit(EXIT_FAILURE);
+    }
+    //assert(l.outputs == params.inputs);
 
     l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1);
-    l.max_boxes = option_find_int_quiet(options, "max", 90);
+    l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0);
+    l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
+    l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0);
+    char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse");   //  "iou");
+
+    if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE;
+    else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU;
+    else l.iou_loss = IOU;
+    fprintf(stderr, "[Gaussian_yolo] iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y);
+
     l.jitter = option_find_float(options, "jitter", .2);
 
     l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
     l.truth_thresh = option_find_float(options, "truth_thresh", 1);
+    l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
     l.random = option_find_int_quiet(options, "random", 0);
 
     char *map_file = option_find_str(options, "map", 0);

src/utils.c

@@ -41,6 +41,7 @@ int *read_map(char *filename)
         map = (int*)realloc(map, n * sizeof(int));
         map[n-1] = atoi(str);
     }
+    if (file) fclose(file);
     return map;
 }
 
@@ -65,6 +66,7 @@ void shuffle(void *arr, size_t n, size_t size)
         memcpy((char*)arr+(j*size), (char*)arr+(i*size), size);
         memcpy((char*)arr+(i*size), swp,          size);
     }
+    free(swp);
 }
 
 void del_arg(int argc, char **argv, int index)
@@ -216,7 +218,7 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output)
     int offset = (p - buffer);
     int chars_from_end = strlen(buffer) - offset;
     if (!p || chars_from_end != strlen(orig)) {  // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'?
-        sprintf(output, "%s", str);
+        sprintf(output, "%s", buffer);
         free(buffer);
         return;
     }
@@ -685,9 +687,9 @@ int max_index(float *a, int n)
 
 int top_max_index(float *a, int n, int k)
 {
+    if (n <= 0) return -1;
     float *values = (float*)calloc(k, sizeof(float));
     int *indexes = (int*)calloc(k, sizeof(int));
-    if (n <= 0) return -1;
     int i, j;
     for (i = 0; i < n; ++i) {
         for (j = 0; j < k; ++j) {

src/yolo.c

@@ -189,6 +189,14 @@ void validate_yolo(char *cfgfile, char *weightfile)
             free_image(val_resized[t]);
         }
     }
+
+    if (fps) free(fps);
+    if (val) free(val);
+    if (val_resized) free(val_resized);
+    if (buf) free(buf);
+    if (buf_resized) free(buf_resized);
+    if (thr) free(thr);
+
     fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
 }
 

src/yolo_layer.c

@@ -128,22 +128,7 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw
     return b;
 }
 
-
-int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id)
-{
-    const float conf_thresh = 0.25;
-
-    int j;
-    for (j = 0; j < classes; ++j) {
-        float prob = objectness * output[class_index + stride*j];
-        if (prob > conf_thresh) {
-            return 1;
-        }
-    }
-    return 0;
-}
-
-ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss)
+ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate)
 {
     ious all_ious = { 0 };
     // i - step in layer width
@@ -162,10 +147,11 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i,
         float tw = log(truth.w*w / biases[2 * n]);
         float th = log(truth.h*h / biases[2 * n + 1]);
 
-        delta[index + 0 * stride] = scale * (tx - x[index + 0 * stride]);
-        delta[index + 1 * stride] = scale * (ty - x[index + 1 * stride]);
-        delta[index + 2 * stride] = scale * (tw - x[index + 2 * stride]);
-        delta[index + 3 * stride] = scale * (th - x[index + 3 * stride]);
+        // accumulate delta
+        delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]);
+        delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]);
+        delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]);
+        delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]);
     }
     else {
         // https://github.com/generalized-iou/g-darknet
@@ -174,25 +160,55 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i,
         all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
 
         // jacobian^t (transpose)
-        delta[index + 0 * stride] = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
-        delta[index + 1 * stride] = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
-        delta[index + 2 * stride] = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
-        delta[index + 3 * stride] = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
+        float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
+        float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
+        float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
+        float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
 
         // predict exponential, apply gradient of e^delta_t ONLY for w,h
-        delta[index + 2 * stride] *= exp(x[index + 2 * stride]);
-        delta[index + 3 * stride] *= exp(x[index + 3 * stride]);
+        dw *= exp(x[index + 2 * stride]);
+        dh *= exp(x[index + 3 * stride]);
 
         // normalize iou weight
-        delta[index + 0 * stride] *= iou_normalizer;
-        delta[index + 1 * stride] *= iou_normalizer;
-        delta[index + 2 * stride] *= iou_normalizer;
-        delta[index + 3 * stride] *= iou_normalizer;
+        dx *= iou_normalizer;
+        dy *= iou_normalizer;
+        dw *= iou_normalizer;
+        dh *= iou_normalizer;
+
+        if (!accumulate) {
+            delta[index + 0 * stride] = 0;
+            delta[index + 1 * stride] = 0;
+            delta[index + 2 * stride] = 0;
+            delta[index + 3 * stride] = 0;
+        }
+
+        // accumulate delta
+        delta[index + 0 * stride] += dx;
+        delta[index + 1 * stride] += dy;
+        delta[index + 2 * stride] += dw;
+        delta[index + 3 * stride] += dh;
     }
 
     return all_ious;
 }
 
+void averages_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
+{
+
+    int classes_in_one_box = 0;
+    int c;
+    for (c = 0; c < classes; ++c) {
+        if (delta[class_index + stride*c] > 0) classes_in_one_box++;
+    }
+
+    if (classes_in_one_box > 0) {
+        delta[box_index + 0 * stride] /= classes_in_one_box;
+        delta[box_index + 1 * stride] /= classes_in_one_box;
+        delta[box_index + 2 * stride] /= classes_in_one_box;
+        delta[box_index + 3 * stride] /= classes_in_one_box;
+    }
+}
+
 void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss)
 {
     int n;
@@ -230,6 +246,19 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int
     }
 }
 
+int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
+{
+    int j;
+    for (j = 0; j < classes; ++j) {
+        //float prob = objectness * output[class_index + stride*j];
+        float prob = output[class_index + stride*j];
+        if (prob > conf_thresh) {
+            return 1;
+        }
+    }
+    return 0;
+}
+
 static int entry_index(layer l, int batch, int location, int entry)
 {
     int n =   location / (l.w*l.h);
@@ -254,6 +283,7 @@ void forward_yolo_layer(const layer l, network_state state)
     }
 #endif
 
+    // delta is zeroed
     memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
     if (!state.train) return;
     //float avg_iou = 0;
@@ -293,7 +323,7 @@ void forward_yolo_layer(const layer l, network_state state)
                         int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                         int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                         float objectness = l.output[obj_index];
-                        int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id);
+                        int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f);
 
                         float iou = box_iou(pred, truth);
                         if (iou > best_match_iou && class_id_match == 1) {
@@ -319,7 +349,7 @@ void forward_yolo_layer(const layer l, network_state state)
                         int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                         delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss);
                         box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
-                        delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss);
+                        delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
                     }
                 }
             }
@@ -353,7 +383,7 @@ void forward_yolo_layer(const layer l, network_state state)
             int mask_n = int_index(l.mask, best_n, l.n);
             if (mask_n >= 0) {
                 int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
-                ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss);
+                ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
 
                 // range is 0 <= 1
                 tot_iou += all_ious.iou;
@@ -376,8 +406,60 @@ void forward_yolo_layer(const layer l, network_state state)
                 if (all_ious.iou > .5) recall += 1;
                 if (all_ious.iou > .75) recall75 += 1;
             }
+
+            // iou_thresh
+            for (n = 0; n < l.total; ++n) {
+                int mask_n = int_index(l.mask, n, l.n);
+                if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) {
+                    box pred = { 0 };
+                    pred.w = l.biases[2 * n] / state.net.w;
+                    pred.h = l.biases[2 * n + 1] / state.net.h;
+                    float iou = box_iou(pred, truth_shift);
+                    // iou, n
+
+                    if (iou > l.iou_thresh) {
+                        int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
+                        ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1);
+
+                        // range is 0 <= 1
+                        tot_iou += all_ious.iou;
+                        tot_iou_loss += 1 - all_ious.iou;
+                        // range is -1 <= giou <= 1
+                        tot_giou += all_ious.giou;
+                        tot_giou_loss += 1 - all_ious.giou;
+
+                        int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
+                        avg_obj += l.output[obj_index];
+                        l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
+
+                        int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
+                        if (l.map) class_id = l.map[class_id];
+                        int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
+                        delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss);
+
+                        ++count;
+                        ++class_count;
+                        if (all_ious.iou > .5) recall += 1;
+                        if (all_ious.iou > .75) recall75 += 1;
+                    }
+                }
+            }
+        }
+
+        // averages the deltas obtained by the function: delta_yolo_box()_accumulate
+        for (j = 0; j < l.h; ++j) {
+            for (i = 0; i < l.w; ++i) {
+                for (n = 0; n < l.n; ++n) {
+                    int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                    int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
+                    const int stride = l.w*l.h;
+
+                    averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
+                }
+            }
         }
     }
+
     //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
     //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f,  count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count);