diff --git a/cfg/yolov3s.cfg b/cfg/yolov3-asff.cfg
similarity index 75%
rename from cfg/yolov3s.cfg
rename to cfg/yolov3-asff.cfg
index 0517b09e..ec47ea3a 100644
--- a/cfg/yolov3s.cfg
+++ b/cfg/yolov3-asff.cfg
@@ -1,3 +1,9 @@
+# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
+# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)):  # from utils.utils import *; kmean_anchors()
+# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00,  2.48s/it]
+# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
+# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
+
 [net]
 # Testing
 # batch=1
@@ -28,7 +34,7 @@ filters=32
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 # Downsample
 
@@ -38,7 +44,7 @@ filters=64
 size=3
 stride=2
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -46,7 +52,7 @@ filters=32
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -54,7 +60,7 @@ filters=64
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -68,7 +74,7 @@ filters=128
 size=3
 stride=2
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -76,7 +82,7 @@ filters=64
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -84,7 +90,7 @@ filters=128
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -96,7 +102,7 @@ filters=64
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -104,7 +110,7 @@ filters=128
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -118,7 +124,7 @@ filters=256
 size=3
 stride=2
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -126,7 +132,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -134,7 +140,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -146,7 +152,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -154,7 +160,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -166,7 +172,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -174,7 +180,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -186,7 +192,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -194,28 +200,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -227,7 +212,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -235,7 +220,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -247,7 +232,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -255,7 +240,7 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -267,7 +252,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -275,7 +260,27 @@ filters=256
 size=3
 stride=1
 pad=1
-activation=swish
+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
@@ -289,7 +294,7 @@ filters=512
 size=3
 stride=2
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -297,7 +302,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -305,70 +310,7 @@ filters=512
 size=3
 stride=1
 pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -380,7 +322,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -388,49 +330,7 @@ filters=512
 size=3
 stride=1
 pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -442,7 +342,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -450,7 +350,107 @@ filters=512
 size=3
 stride=1
 pad=1
-activation=swish
+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
@@ -464,7 +464,7 @@ filters=1024
 size=3
 stride=2
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -472,7 +472,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -480,7 +480,7 @@ filters=1024
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -492,7 +492,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -500,7 +500,7 @@ filters=1024
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -512,7 +512,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -520,7 +520,7 @@ filters=1024
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -532,7 +532,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -540,7 +540,7 @@ filters=1024
 size=3
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [shortcut]
 from=-3
@@ -554,7 +554,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -562,7 +562,7 @@ size=3
 stride=1
 pad=1
 filters=1024
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -570,9 +570,9 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
-### SPP ###
+# SPP --------------------------------------------------------------------------
 [maxpool]
 stride=1
 size=5
@@ -593,8 +593,7 @@ size=13
 
 [route]
 layers=-1,-3,-5,-6
-
-### End SPP ###
+# SPP --------------------------------------------------------------------------
 
 [convolutional]
 batch_normalize=1
@@ -602,8 +601,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
-
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -611,7 +609,7 @@ size=3
 stride=1
 pad=1
 filters=1024
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -619,7 +617,7 @@ filters=512
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -627,29 +625,19 @@ size=3
 stride=1
 pad=1
 filters=1024
-activation=swish
+activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=255
+filters=258
 activation=linear
 
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
+# YOLO -------------------------------------------------------------------------
 
 [route]
-layers = -4
+layers = -3
 
 [convolutional]
 batch_normalize=1
@@ -657,7 +645,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [upsample]
 stride=2
@@ -665,15 +653,13 @@ stride=2
 [route]
 layers = -1, 61
 
-
-
 [convolutional]
 batch_normalize=1
 filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -681,7 +667,7 @@ size=3
 stride=1
 pad=1
 filters=512
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -689,7 +675,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -697,7 +683,7 @@ size=3
 stride=1
 pad=1
 filters=512
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -705,7 +691,7 @@ filters=256
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -713,30 +699,19 @@ size=3
 stride=1
 pad=1
 filters=512
-activation=swish
+activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=255
+filters=258
 activation=linear
 
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
+# YOLO -------------------------------------------------------------------------
 
 [route]
-layers = -4
+layers = -3
 
 [convolutional]
 batch_normalize=1
@@ -744,7 +719,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [upsample]
 stride=2
@@ -752,15 +727,13 @@ stride=2
 [route]
 layers = -1, 36
 
-
-
 [convolutional]
 batch_normalize=1
 filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -768,7 +741,7 @@ size=3
 stride=1
 pad=1
 filters=256
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -776,7 +749,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -784,7 +757,7 @@ size=3
 stride=1
 pad=1
 filters=256
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -792,7 +765,7 @@ filters=128
 size=1
 stride=1
 pad=1
-activation=swish
+activation=leaky
 
 [convolutional]
 batch_normalize=1
@@ -800,22 +773,32 @@ size=3
 stride=1
 pad=1
 filters=256
-activation=swish
+activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=255
+filters=258
 activation=linear
 
-
 [yolo]
-mask = 0,1,2
+from=88,99,110
+mask = 8,9,10,11
 anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
 classes=80
 num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
+
+[yolo]
+from=88,99,110
+mask = 4,5,6,7
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+
+[yolo]
+from=88,99,110
+mask = 0,1,2,3
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
\ No newline at end of file
diff --git a/cfg/yolov4-tiny-1cls.cfg b/cfg/yolov4-tiny-1cls.cfg
deleted file mode 100644
index 7cf2dd4e..00000000
--- a/cfg/yolov4-tiny-1cls.cfg
+++ /dev/null
@@ -1,233 +0,0 @@
-# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
-# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)):  # from utils.utils import *; kmean_anchors()
-# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00,  2.48s/it]
-# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
-# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-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 = 200000
-policy=steps
-steps=180000,190000
-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=24
-activation=linear
-
-
-
-[yolo]
-mask = 8,9,10,11
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=1
-num=12
-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=24
-activation=linear
-
-[yolo]
-mask = 4,5,6,7
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=1
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 6
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=24
-activation=linear
-
-[yolo]
-mask = 0,1,2,3
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=1
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/cfg/yolov4-tiny.cfg b/cfg/yolov4-tiny.cfg
deleted file mode 100644
index 5548ca60..00000000
--- a/cfg/yolov4-tiny.cfg
+++ /dev/null
@@ -1,233 +0,0 @@
-# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
-# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)):  # from utils.utils import *; kmean_anchors()
-# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00,  2.48s/it]
-# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
-# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-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 = 200000
-policy=steps
-steps=180000,190000
-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=340
-activation=linear
-
-
-
-[yolo]
-mask = 8,9,10,11
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=80
-num=12
-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=340
-activation=linear
-
-[yolo]
-mask = 4,5,6,7
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=80
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 6
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=linear
-
-[yolo]
-mask = 0,1,2,3
-anchors = 6,9,  15,16,  17,35,  37,26,  36,67,  63,42,  57,100,  121,81,  112,169,  241,158,  195,310,  426,359
-classes=80
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1