C:\Users\karay\darknet>darknet detector train cfg/plate-obj.data cfg/plate-yolov3.cfg darknet53.conv.74
plate-yolov3
net.optimized_memory = 0
batch = 1, time_steps = 1, train = 1
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
1 conv 64 3 x 3/ 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF
2 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
3 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
4 Shortcut Layer: 1
5 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
6 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
7 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
8 Shortcut Layer: 5
9 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
10 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
11 Shortcut Layer: 8
12 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
13 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
14 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
15 Shortcut Layer: 12
16 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
17 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
18 Shortcut Layer: 15
19 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
20 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
21 Shortcut Layer: 18
22 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
23 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
24 Shortcut Layer: 21
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
27 Shortcut Layer: 24
28 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
29 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
30 Shortcut Layer: 27
31 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
32 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
33 Shortcut Layer: 30
34 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
35 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
36 Shortcut Layer: 33
37 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
38 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
39 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
40 Shortcut Layer: 37
41 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
42 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
43 Shortcut Layer: 40
44 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
45 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
46 Shortcut Layer: 43
47 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
48 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
49 Shortcut Layer: 46
50 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
51 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
52 Shortcut Layer: 49
53 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
54 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
55 Shortcut Layer: 52
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
58 Shortcut Layer: 55
59 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
60 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
61 Shortcut Layer: 58
62 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
63 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
64 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
65 Shortcut Layer: 62
66 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
67 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
68 Shortcut Layer: 65
69 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
70 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
71 Shortcut Layer: 68
72 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
73 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
74 Shortcut Layer: 71
75 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
76 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
77 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
78 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
79 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
80 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
81 conv 18 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 18 0.006 BF
82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
83 route 79 -> 13 x 13 x 512
84 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61 -> 26 x 26 x 768
87 conv 256 1 x 1/ 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF
88 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
89 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
90 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
91 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
92 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
93 conv 18 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 18 0.012 BF
94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
95 route 91 -> 26 x 26 x 256
96 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36 -> 52 x 52 x 384
99 conv 128 1 x 1/ 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF
100 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
101 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
102 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
103 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
104 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
105 conv 18 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 18 0.025 BF
106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.290
Allocate additional workspace_size = 12.46 MB
Loading weights from darknet53.conv.74...
seen 64
Done! Loaded 75 layers from weights-file
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
If error occurs - run training with flag: -dont_show
Resizing
608 x 608
used slow CUDNN algo without Workspace! Need memory: 6653952, available: 6229196
CUDNN-slow Try to set subdivisions=64 in your cfg-file.
CUDA status Error: file: C:\Users\karay\darknet\src\dark_cuda.c : cuda_make_array() : line: 362 : build time: Dec 24 2019 - 22:34:53
CUDA Error: out of memory
My plate-yolov3.cfg file is like below
[net]
# Testing
batch=64
subdivisions=64
# Training
# batch=64
# subdivisions=64
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 4000
policy=steps
steps=4800,5400
scales=.1,.1
[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=18
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=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
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=18
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=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 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=18
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
ogulcankarayel5
All 4 comments
Set random=0 in the last [yolo] layer in cfg-file
If it doesn't help, also set width=320 height=320
AlexeyAB
on 24 Dec 2019
Is this alright
C:\Users\karay\darknet>darknet.exe detector train cfg/plate-obj.data cfg/plate-yolov3.cfg darknet53.conv.74
plate-yolov3
net.optimized_memory = 0
batch = 1, time_steps = 1, train = 1
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 320 x 320 x 3 -> 320 x 320 x 32 0.177 BF
1 conv 64 3 x 3/ 2 320 x 320 x 32 -> 160 x 160 x 64 0.944 BF
2 conv 32 1 x 1/ 1 160 x 160 x 64 -> 160 x 160 x 32 0.105 BF
3 conv 64 3 x 3/ 1 160 x 160 x 32 -> 160 x 160 x 64 0.944 BF
4 Shortcut Layer: 1
5 conv 128 3 x 3/ 2 160 x 160 x 64 -> 80 x 80 x 128 0.944 BF
6 conv 64 1 x 1/ 1 80 x 80 x 128 -> 80 x 80 x 64 0.105 BF
7 conv 128 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 128 0.944 BF
8 Shortcut Layer: 5
9 conv 64 1 x 1/ 1 80 x 80 x 128 -> 80 x 80 x 64 0.105 BF
10 conv 128 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 128 0.944 BF
11 Shortcut Layer: 8
12 conv 256 3 x 3/ 2 80 x 80 x 128 -> 40 x 40 x 256 0.944 BF
13 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
14 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
15 Shortcut Layer: 12
16 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
17 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
18 Shortcut Layer: 15
19 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
20 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
21 Shortcut Layer: 18
22 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
23 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
24 Shortcut Layer: 21
25 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
26 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
27 Shortcut Layer: 24
28 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
29 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
30 Shortcut Layer: 27
31 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
32 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
33 Shortcut Layer: 30
34 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
35 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
36 Shortcut Layer: 33
37 conv 512 3 x 3/ 2 40 x 40 x 256 -> 20 x 20 x 512 0.944 BF
38 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
39 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
40 Shortcut Layer: 37
41 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
42 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
43 Shortcut Layer: 40
44 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
45 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
46 Shortcut Layer: 43
47 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
48 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
49 Shortcut Layer: 46
50 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
51 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
52 Shortcut Layer: 49
53 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
54 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
55 Shortcut Layer: 52
56 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
57 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
58 Shortcut Layer: 55
59 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
60 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
61 Shortcut Layer: 58
62 conv 1024 3 x 3/ 2 20 x 20 x 512 -> 10 x 10 x1024 0.944 BF
63 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
64 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
65 Shortcut Layer: 62
66 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
67 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
68 Shortcut Layer: 65
69 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
70 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
71 Shortcut Layer: 68
72 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
73 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
74 Shortcut Layer: 71
75 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
76 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
77 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
78 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
79 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
80 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
81 conv 18 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 18 0.004 BF
82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
83 route 79 -> 10 x 10 x 512
84 conv 256 1 x 1/ 1 10 x 10 x 512 -> 10 x 10 x 256 0.026 BF
85 upsample 2x 10 x 10 x 256 -> 20 x 20 x 256
86 route 85 61 -> 20 x 20 x 768
87 conv 256 1 x 1/ 1 20 x 20 x 768 -> 20 x 20 x 256 0.157 BF
88 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
89 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
90 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
91 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
92 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
93 conv 18 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 18 0.007 BF
94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
95 route 91 -> 20 x 20 x 256
96 conv 128 1 x 1/ 1 20 x 20 x 256 -> 20 x 20 x 128 0.026 BF
97 upsample 2x 20 x 20 x 128 -> 40 x 40 x 128
98 route 97 36 -> 40 x 40 x 384
99 conv 128 1 x 1/ 1 40 x 40 x 384 -> 40 x 40 x 128 0.157 BF
100 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
101 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
102 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
103 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
104 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
105 conv 18 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 18 0.015 BF
106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 38.633
Allocate additional workspace_size = 7.37 MB
Loading weights from darknet53.conv.74...
seen 64
Done! Loaded 75 layers from weights-file
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
If error occurs - run training with flag: -dont_showC:\Users\karay\darknet>darknet53.convdarknet.exe detector train cfg/plate-obj.data cfg/plate-yolov3.cfg darknet53.conv.74 -dont_show
'darknet53.convdarknet.exe' is not recognized as an internal or external command,
operable program or batch file.C:\Users\karay\darknet>darknet.exe detector train cfg/plate-obj.data cfg/plate-yolov3.cfg darknet53.conv.74
plate-yolov3
net.optimized_memory = 0
batch = 1, time_steps = 1, train = 1
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 320 x 320 x 3 -> 320 x 320 x 32 0.177 BF
1 conv 64 3 x 3/ 2 320 x 320 x 32 -> 160 x 160 x 64 0.944 BF
2 conv 32 1 x 1/ 1 160 x 160 x 64 -> 160 x 160 x 32 0.105 BF
3 conv 64 3 x 3/ 1 160 x 160 x 32 -> 160 x 160 x 64 0.944 BF
4 Shortcut Layer: 1
5 conv 128 3 x 3/ 2 160 x 160 x 64 -> 80 x 80 x 128 0.944 BF
6 conv 64 1 x 1/ 1 80 x 80 x 128 -> 80 x 80 x 64 0.105 BF
7 conv 128 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 128 0.944 BF
8 Shortcut Layer: 5
9 conv 64 1 x 1/ 1 80 x 80 x 128 -> 80 x 80 x 64 0.105 BF
10 conv 128 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 128 0.944 BF
11 Shortcut Layer: 8
12 conv 256 3 x 3/ 2 80 x 80 x 128 -> 40 x 40 x 256 0.944 BF
13 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
14 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
15 Shortcut Layer: 12
16 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
17 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
18 Shortcut Layer: 15
19 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
20 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
21 Shortcut Layer: 18
22 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
23 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
24 Shortcut Layer: 21
25 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
26 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
27 Shortcut Layer: 24
28 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
29 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
30 Shortcut Layer: 27
31 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
32 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
33 Shortcut Layer: 30
34 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
35 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
36 Shortcut Layer: 33
37 conv 512 3 x 3/ 2 40 x 40 x 256 -> 20 x 20 x 512 0.944 BF
38 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
39 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
40 Shortcut Layer: 37
41 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
42 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
43 Shortcut Layer: 40
44 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
45 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
46 Shortcut Layer: 43
47 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
48 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
49 Shortcut Layer: 46
50 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
51 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
52 Shortcut Layer: 49
53 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
54 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
55 Shortcut Layer: 52
56 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
57 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
58 Shortcut Layer: 55
59 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
60 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
61 Shortcut Layer: 58
62 conv 1024 3 x 3/ 2 20 x 20 x 512 -> 10 x 10 x1024 0.944 BF
63 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
64 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
65 Shortcut Layer: 62
66 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
67 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
68 Shortcut Layer: 65
69 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
70 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
71 Shortcut Layer: 68
72 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
73 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
74 Shortcut Layer: 71
75 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
76 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
77 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
78 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
79 conv 512 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 512 0.105 BF
80 conv 1024 3 x 3/ 1 10 x 10 x 512 -> 10 x 10 x1024 0.944 BF
81 conv 18 1 x 1/ 1 10 x 10 x1024 -> 10 x 10 x 18 0.004 BF
82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
83 route 79 -> 10 x 10 x 512
84 conv 256 1 x 1/ 1 10 x 10 x 512 -> 10 x 10 x 256 0.026 BF
85 upsample 2x 10 x 10 x 256 -> 20 x 20 x 256
86 route 85 61 -> 20 x 20 x 768
87 conv 256 1 x 1/ 1 20 x 20 x 768 -> 20 x 20 x 256 0.157 BF
88 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
89 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
90 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
91 conv 256 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 256 0.105 BF
92 conv 512 3 x 3/ 1 20 x 20 x 256 -> 20 x 20 x 512 0.944 BF
93 conv 18 1 x 1/ 1 20 x 20 x 512 -> 20 x 20 x 18 0.007 BF
94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
95 route 91 -> 20 x 20 x 256
96 conv 128 1 x 1/ 1 20 x 20 x 256 -> 20 x 20 x 128 0.026 BF
97 upsample 2x 20 x 20 x 128 -> 40 x 40 x 128
98 route 97 36 -> 40 x 40 x 384
99 conv 128 1 x 1/ 1 40 x 40 x 384 -> 40 x 40 x 128 0.157 BF
100 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
101 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
102 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
103 conv 128 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 128 0.105 BF
104 conv 256 3 x 3/ 1 40 x 40 x 128 -> 40 x 40 x 256 0.944 BF
105 conv 18 1 x 1/ 1 40 x 40 x 256 -> 40 x 40 x 18 0.015 BF
106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 38.633
Allocate additional workspace_size = 7.37 MB
Loading weights from darknet53.conv.74...
seen 64
Done! Loaded 75 layers from weights-file
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
If error occurs - run training with flag: -dont_show
ogulcankarayel5
on 24 Dec 2019
batch / subdivisions is the amount of VRAM darknet will use when training
k00r
on 15 Jan 2020
I had this same issue. Try to modify batch and subdivisions params first. If that does not help, change the width and height params.
My current config file is this, after tweaking multiple times to not get a CUDA Out of Memory error.
Testing
batch=8
subdivisions=64
Training
batch=8
subdivisions=64
width=320
height=320
bidyutchanda
on 21 Feb 2020
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Most helpful comment
Set random=0 in the last [yolo] layer in cfg-file
If it doesn't help, also set width=320 height=320