updates
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# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
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# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
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# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
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# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
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# 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
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[net]
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# Testing
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# batch=1
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# subdivisions=1
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# Training
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batch=64
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subdivisions=16
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width=608
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height=608
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 200000
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policy=steps
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steps=180000,190000
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scales=.1,.1
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[convolutional]
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batch_normalize=1
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filters=16
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=1
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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###########
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=24
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activation=linear
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[yolo]
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mask = 8,9,10,11
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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
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classes=1
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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[route]
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layers = -4
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[upsample]
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stride=2
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[route]
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layers = -1, 8
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=24
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activation=linear
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[yolo]
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mask = 4,5,6,7
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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
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classes=1
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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[route]
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layers = -3
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[upsample]
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stride=2
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[route]
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layers = -1, 6
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=24
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activation=linear
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[yolo]
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mask = 0,1,2,3
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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
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classes=1
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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@ -0,0 +1,233 @@
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# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
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# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
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# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
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# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
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# 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
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[net]
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# Testing
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# batch=1
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# subdivisions=1
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# Training
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batch=64
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subdivisions=16
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width=608
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height=608
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 200000
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policy=steps
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steps=180000,190000
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scales=.1,.1
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[convolutional]
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batch_normalize=1
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filters=16
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=2
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[maxpool]
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size=2
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stride=1
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[convolutional]
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batch_normalize=1
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filters=1024
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size=3
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stride=1
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pad=1
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activation=leaky
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###########
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=340
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activation=linear
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[yolo]
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mask = 8,9,10,11
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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
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classes=80
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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[route]
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layers = -4
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[upsample]
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stride=2
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[route]
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layers = -1, 8
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=340
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activation=linear
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[yolo]
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mask = 4,5,6,7
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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
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classes=80
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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[route]
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layers = -3
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[upsample]
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stride=2
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[route]
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layers = -1, 6
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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size=1
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stride=1
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pad=1
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filters=340
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activation=linear
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[yolo]
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mask = 0,1,2,3
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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
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classes=80
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num=12
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jitter=.3
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ignore_thresh = .7
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truth_thresh = 1
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random=1
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