Open eltonfernando opened 4 years ago
I am having this same error.
Issuing command to train custom detector(in darknet directory): !./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg yolov4.conv.137 -dont_show -map
This is what shows in command line: training_error.pdf
No jpg or txt file in train or validation with this name - data/obj/labelsImage_jpg.rf.65322d152e11c328217e630364f8e5f5.txt
I've searched my whole device, I have no idea where it is getting this from. If anyone knows how to resolve this, please let me know! :)
Is your obj.data file something like this?
classes = 1
train = data/train.txt
valid = data/valid.txt
names = data/people.names
backup = backup/
do you have an image with that name? labelsImage_jpg.rf.65322d152e11c328217e630364f8e5f5 do you have an image with that name? if yes you need txt for her
That image does not exist within my test/train/or valid folders. I am not sure where it is coming from. Maybe a bug? This is my obj.data:
classes = 3
train = data/train.txt
valid = data/valid.txt
names = data/obj.names
backup = backup/
On Fri, Oct 30, 2020 at 5:39 PM Elton fernandes dos santos < notifications@github.com> wrote:
Is your obj.data file something like this?
classes = 1 train = data/train.txt valid = data/valid.txt names = data/people.names backup = backup/
do you have an image with that name? labelsImage_jpg.rf.65322d152e11c328217e630364f8e5f5 do you have an image with that name? if yes you need txt for her
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This could be an error in the .cfg file for 3 classes (yolov3)
this could be an error in the .cfg file
did you change the filters of the last layer to 24?and set classes for 3
[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
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 = 60000
policy=steps
steps=4800,4500
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=24
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=3
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
I am using Yolov4. For my dataset, I only have 2 classes (images with people wearing masks and people wearing no masks). So in obj.data I changed classes = 2. Still getting the same error message. This is my cfg file (attached). I've also included a link to my training configuration script if that helps... https://github.com/vischulisem/YOLOv4_Project/blob/master/training_config.py
On Fri, Oct 30, 2020 at 9:50 PM Elyse Vischulis vischulisem@gmail.com wrote:
I am using Yolov4. For my dataset, I only have 2 classes (images with people wearing masks and people wearing no masks). So in obj.data I changed classes = 2. Still getting the same error message. This is my cfg file (attached). I've also included a link to my training configuration script if that helps... https://github.com/vischulisem/YOLOv4_Project/blob/master/training_config.py
On Fri, Oct 30, 2020 at 9:17 PM Elton fernandes dos santos < notifications@github.com> wrote:
This could be an error in the .cfg file for 3 classes (yolov3)
this could be an error in the .cfg file
did you change the filters of the last layer to 24?and set classes for 3
[net] Testing
batch=1 subdivisions=1 Training batch=64 subdivisions=2
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 = 60000 policy=steps steps=4800,4500 scales=.1,.1
[convolutional] batch_normalize=1 filters=16 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky
[maxpool] size=2 stride=1
[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky
###########
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky
[convolutional] size=1 stride=1 pad=1 filters=24 activation=linear
[yolo] mask = 3,4,5 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=24 num=6 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1
[route] layers = -4
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[upsample] stride=2
[route] layers = -1, 8
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] size=1 stride=1 pad=1 filters=24 activation=linear
[yolo] mask = 0,1,2 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=3 num=6 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1
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I was unable to build using your script, it seems that it depends on several files yolov4-custom
can you show me yours cfg/custom-yolov4-detector.cfg?
custom-yolov4-detector.cfg
[net] batch=64 subdivisions=24 width=416 height=416 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=4000 policy=steps steps=3200.0,3600.0 scales=.1,.1
mosaic=1
[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=mish
[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
[convolutional] batch_normalize=1 filters=32 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
[route] layers = -1,-7
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=mish
[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
[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
[route] layers = -1,-10
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=mish
[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
[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
[route] layers = -1,-28
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=mish
[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
[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
[route] layers = -1,-28
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=1024 size=3 stride=2 pad=1 activation=mish
[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 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
[route] layers = -1,-16
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=mish
##########################
[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
[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
[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 filters=256 size=1 stride=1 pad=1 activation=leaky
[upsample] stride=2
[route] layers = 85
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[route] layers = -1, -3
[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 filters=128 size=1 stride=1 pad=1 activation=leaky
[upsample] stride=2
[route] layers = 54
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[route] layers = -1, -3
[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=21 activation=linear
[yolo] mask = 0,1,2 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5
[route] layers = -4
[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=256 activation=leaky
[route] layers = -1, -16
[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=21 activation=linear
[yolo] mask = 3,4,5 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5
[route] layers = -4
[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=512 activation=leaky
[route] layers = -1, -37
[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=21 activation=linear
[yolo] mask = 6,7,8 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1 scale_x_y = 1.05 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5
On Fri, Oct 30, 2020 at 10:25 PM Elton fernandes dos santos < notifications@github.com> wrote:
I was unable to build using your script, it seems that it depends on several files yolov4-custom .cfg
can you show me yours cfg/custom-yolov4-detector.cfg?
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ok, one observations
I believe you calculated max_batches = (classes * 2000) =4000
this is valid only when the result is greater than 6000
when you are less you ignore and use 6000
then it would stay
max_batches=6000
steps=4800,5400
4800=6000 0.8 # 80% 5400=6000 0.9 # 90%
Ah good observation. I changed max_batches and steps in config file...however still getting error:
Loading weights from yolov4.conv.137...Done!
Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005
Resizing
512
Couldn't open file: data/obj/labelsImage_jpg.rf.65322d152e11c328217e630364f8e5f5.txt
On Fri, Oct 30, 2020 at 10:51 PM Elton fernandes dos santos < notifications@github.com> wrote:
ok, two observations
I believe you calculated max_batches = (classes * 2000) =4000
this is valid only when the result is greater than 6000
when you are less you ignore and use 6000
then it would stay
max_batches=6000
steps=4800,5400
4800=6000 0.8 # 80% 5400=60000.9 # 90%
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very strange, I have a question, you have 2 classes correct?
your file obj/data.data say 3 https://github.com/pjreddie/darknet/issues/2289#issuecomment-719835846 your file cfg/custom-yolov4-detector.cfg say 2
may be you should noted the makefile file “opencv =0” and the label txt file "unix" not "windows" format in end-of-line.
I'm training a custom date. I get this error
the strange thing is that this file (labelsImage_jpg.rf.8bdbe22463c3162f05180ff91e67f039.txt) is not on my train.txt list.
I also scanned my project folder for labelsimage_* , and that name doesn't exist anywhere. no idea where this is coming from.
sometimes the error changes Couldn't open file: sars-gettylabels-157005245_jpg.rf.5f33a6bd1f780b98e348507f3835d4d6.txt
sometimes runs for a few iterations and stop.