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hello. I have a question about the darknet yolov4 error. #2648

Open
chanhui6279 opened this issue Jan 9, 2025 · 1 comment
Open

hello. I have a question about the darknet yolov4 error. #2648

chanhui6279 opened this issue Jan 9, 2025 · 1 comment

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@chanhui6279
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hello. I am a developer who is just starting to use darknet. I created an environment for learning the model and am now trying to learn it, but an error occurred during the learning process, so I am asking this question.
It may be a bit inconvenient because the translation is being done using a translator.

The code I am showing from now on is yolov4-p6.cfg.
`[net]

Testing
#batch=1
#subdivisions=1

Training
batch=64
subdivisions=16
width=64
height=64
channels=3
momentum=0.949
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
burn_in=1000
max_batches = 700
policy=steps
steps=400,450
scales=.1,.1

mosaic=1

letter_box=1

ema_alpha=0.9998

#use_cuda_graph = 1

============ Backbone ============
Stem
0
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=mish

P1
Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-7

Transition last
10 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

P2
Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-13

Transition last
26 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

P3
Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-49

Transition last
78 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

P4
Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-49

Transition last
130 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

P5
Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-25

Transition last
158 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=mish

P6
Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Residual Block
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=mish

[shortcut]
from=-3
activation=linear

Transition first
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Merge [-1, -(3k+4)]
[route]
layers = -1,-25

Transition last
186 (previous+7+3k)
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=mish

============ End of Backbone ============
============ Neck ============
CSPSPP
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

SPP
[maxpool]
stride=1
size=5

[route]
layers=-2

[maxpool]
stride=1
size=9

[route]
layers=-4

[maxpool]
stride=1
size=13

[route]
layers=-1,-3,-5,-6

End SPP
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[route]
layers = -1, -13

201 (previous+6+5+2k)
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

End of CSPSPP
FPN-5
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[upsample]
stride=2

[route]
layers = 158

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -1, -3

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

Merge [-1, -(2k+2)]
[route]
layers = -1, -8

Transition last
217 (previous+6+4+2k)
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

FPN-4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[upsample]
stride=2

[route]
layers = 130

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[route]
layers = -1, -3

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

Merge [-1, -(2k+2)]
[route]
layers = -1, -8

Transition last
233 (previous+6+4+2k)
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

FPN-3
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[upsample]
stride=2

[route]
layers = 78

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[route]
layers = -1, -3

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=mish

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=mish

Merge [-1, -(2k+2)]
[route]
layers = -1, -8

Transition last
249 (previous+6+4+2k)
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=mish

PAN-4
[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=256
activation=mish

[route]
layers = -1, 233

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=mish

[route]
layers = -1,-8

Transition last
262 (previous+3+4+2k)
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=mish

PAN-5
[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=512
activation=mish

[route]
layers = -1, 217

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[route]
layers = -1,-8

Transition last
275 (previous+3+4+2k)
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

PAN-6
[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=512
activation=mish

[route]
layers = -1, 201

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

Split
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[route]
layers = -2

Plain Block
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=mish

[route]
layers = -1,-8

Transition last
288 (previous+3+4+2k)
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=mish

============ End of Neck ============
============ Head ============
YOLO-3
[route]
layers = 249

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=21
activation=mish

[convolutional]
size=1
stride=1
pad=1
filters=21
activation=logistic
#activation=linear

use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
[yolo]
mask = 0,1,2,3
anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024
classes=2
num=16
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
#random=1
resize=1.5
iou_thresh=0.2
iou_normalizer=0.05
cls_normalizer=0.5
obj_normalizer=1.0
iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=2

YOLO-4
[route]
layers = 262

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=21
activation=mish

[convolutional]
size=1
stride=1
pad=1
filters=21
activation=logistic
#activation=linear

use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
[yolo]
mask = 4,5,6,7
anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024
classes=2
num=16
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
#random=1
resize=1.5
iou_thresh=0.2
iou_normalizer=0.05
cls_normalizer=0.5
obj_normalizer=1.0
iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=2

YOLO-5
[route]
layers = 275

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=21
activation=mish

[convolutional]
size=1
stride=1
pad=1
filters=21
activation=logistic
#activation=linear

use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
[yolo]
mask = 8,9,10,11
anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024
classes=2
num=16
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
#random=1
resize=1.5
iou_thresh=0.2
iou_normalizer=0.05
cls_normalizer=0.5
obj_normalizer=1.0
iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=2

YOLO-6
[route]
layers = 288

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=21
activation=mish

[convolutional]
size=1
stride=1
pad=1
filters=21
activation=logistic
#activation=linear

use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
[yolo]
mask = 12,13,14,15
anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024
classes=2
num=16
jitter=.1
scale_x_y = 2.0
objectness_smooth=1
ignore_thresh = .7
truth_thresh = 1
#random=1
resize=1.5
iou_thresh=0.2
iou_normalizer=0.05
cls_normalizer=0.5
obj_normalizer=1.0
iou_loss=ciou
nms_kind=diounms
beta_nms=0.6
new_coords=1
max_delta=2

============ End of Head ============ #`
Error: l.outputs == params.inputs, filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer: No error

This is the exact error, but I'm not sure why it appears.
please help me.

If you have any questions, please ask and I will answer.

thx
@chanhui6279
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There are two classes

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