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