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Error in load_data_detection() - OpenCV when training on my custom data in windows 10 #2237

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asdsdlgl opened this issue Jul 20, 2020 · 30 comments
Open

Error in load_data_detection() - OpenCV when training on my custom data in windows 10 #2237

asdsdlgl opened this issue Jul 20, 2020 · 30 comments

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@asdsdlgl
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asdsdlgl commented Jul 20, 2020 •
edited
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I have the issue when i train my custom data in windows 10, it will show the error " Error in load_data_detection() - OpenCV " when loading the image. And if i just test data using pretrained-weight not training, it will correctly detect the result.
And i try different versions of opencv, including 3.4.5 3.4.10 4.1.0, all of them are failed. My cuda version is 10.0 and vs2017.
How to resolve this problem that " Error in load_data_detection() - OpenCV " when training custom data?
@sharoseali
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sharoseali commented Aug 21, 2020 •
edited
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HI, @asdsdlgl did you manage this problem. I am also stucked here... please help... All my images and their annotations are are in the correct path, but still don't know why its happening.. @AlexeyAB
I am using this command darknet.exe detector train data/drone.data cfg/yolov4-tiny_drone.cfg yolov4-tiny.conv.29 and results are like this
image

@MohamedAdelNaguib
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I have the issue when i train my custom data in windows 10, it will show the error " Error in load_data_detection() - OpenCV " when loading the image. And if i just test data using pretrained-weight not training, it will correctly detect the result.
And i try different versions of opencv, including 3.4.5 3.4.10 4.1.0, all of them are failed. My cuda version is 10.0 and vs2017.
How to resolve this problem that " Error in load_data_detection() - OpenCV " when training custom data?

Hi , I am also stuck in this error anyone have a solution?

@sharoseali
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sharoseali commented Aug 24, 2020 •
edited
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OK, the error is resolved when I again generate the train and valid.txt files by writing the absolute path of all image files. Here is the code:

from os import listdir
from os.path import isfile, join
customPath = 'Your/abs/path/' 
onlyfiles = [f for f in listdir(customPath + '/DET-val/data/') if isfile(join(customPath + '/DET-val/data',f))]


trainFile = customPath + "/val.lists"
file = open(trainFile, 'w')


counter = 0
customPath = 'Your/abs/path/' 
for eachFile in onlyfiles:
	if "jpg" in eachFile:
		counter+=1
		file.write(customPath + eachFile + "\n")
		

print(counter)

@ganapathi12
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OK, the error is resolved when I again generate the train and valid.txt files by writing the absolute path of all image files. Here is the code:

from os import listdir
from os.path import isfile, join
customPath = 'Your/abs/path/' 
onlyfiles = [f for f in listdir(customPath + '/DET-val/data/') if isfile(join(customPath + '/DET-val/data',f))]


trainFile = customPath + "/val.lists"
file = open(trainFile, 'w')


counter = 0
customPath = 'Your/abs/path/' 
for eachFile in onlyfiles:
	if "jpg" in eachFile:
		counter+=1
		file.write(customPath + eachFile + "\n")
		

print(counter)

hi! i am a newbie, even i am getting the same error and i use the below code for generating train and test files could you help me by making changes to below code:

import os
import random

imgspath = 'C:/yolo_v4/yolo_v4_mask_detection/darknet/build/darknet/x64/data/obj'
path = 'data/obj/'


images = []
for i in os.listdir(imgspath):
    temp = path+i
    images.append(temp)
# train and test split... adjust it if necessary
trainlen = round(len(images)*.80)
testlen = round(len(images)*.20)
#print('total, train, test dataset size -',trainlen+testlen,trainlen,testlen)
random.shuffle(images)
test = images[:testlen]
train = images[testlen:]

with open('train.txt', 'w') as f:
    for item in train:
        f.write("%s\n" % item)
with open('test.txt', 'w') as f:
    for item in test:
        f.write("%s\n" % item)

@ganapathi12
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@sharoseali

@Shovankumarp
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I have the same problem couldn't solve yet. Could anyone help me please.

@MysticCoss
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I have the same problem here. All i did is DOUBLE CHECK the path or the images

@joseporiolayats
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Hi I had the same problem and I solved it by cloning darknet into a different directory (and compiling, and executing). I'm using c:\darknet as my base directory and now everything works. In my case I suspect it was related to my directories having spaces in the names.

Also I rechecked that all directories were marked with a \ and not \ or /.

I hope it may help.

@giangnd1808
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because it's no data

@SankalppPanghal
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I changed my input images from 3 channel image (normal RGB image) to 6 channel image. Because I want to train on 6 channel image.
But it seems Yolo is not prepared to take in more than 3 or 4 channel image? I am able to change yolov4-obg.cfg file and set channels = 6, but during reading, it cannot read, same error as this one...........

Anyone if have tried to train on more than 3 channel image, please let know, thanks

@bjajoh
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bjajoh commented Apr 26, 2021

For everyone on ubuntu having this issue, just put your labels and images folder in the darknet folder. That works for me!

@Hezhexi2002
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Hi I had the same problem and I solved it by cloning darknet into a different directory (and compiling, and executing). I'm using c:\darknet as my base directory and now everything works. In my case I suspect it was related to my directories having spaces in the names.

Also I rechecked that all directories were marked with a \ and not \ or /.

I hope it may help.
Did you mean all the paths in the train.txt and test.txt should be marked with a \ instead of a /?

@Nakkhatra
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Nakkhatra commented Jul 27, 2021 •
edited
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Well the error is actually related to your train.txt file. If you put in some directory of the train images in the train.txt file where it cannot find/read the image, then this error shows up. Please check the directory again if you are facing this.

@Hezhexi2002
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Thanks a lot，I have solved it already

@Nakkhatra
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Glad to hear that, actually I ran into the same problem yesterday and later I found out that I mistakenly put the image paths of the validation set into the train.txt.

@v4vshnu
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v4vshnu commented Sep 10, 2021

I encountered the same problem, solved it by giving the absolute path of the image folder in the code.

@Shayendra
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Please share the code

@Shayendra
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how to solve this problem please help me
code

!./darknet detector train data/multiple_images.data cfg/yolov4-custom.cfg yolov4.conv.137 -dont_show
CUDA-version: 11010 (11020), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 3.2.0
yolov4-custom
0 : compute_capability = 370, cudnn_half = 0, GPU: Tesla K80
net.optimized_memory = 0
mini_batch = 4, batch = 64, time_steps = 1, train = 1
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 32 3 x 3/ 2 416 x 416 x 4 -> 208 x 208 x 32 0.100 BF
1 conv 64 3 x 3/ 2 208 x 208 x 32 -> 104 x 104 x 64 0.399 BF
2 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
3 route 2 1/2 -> 104 x 104 x 32
4 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF
5 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF
6 route 5 4 -> 104 x 104 x 64
7 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
8 route 2 7 -> 104 x 104 x 128
9 max 2x 2/ 2 104 x 104 x 128 -> 52 x 52 x 128 0.001 BF
10 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
11 route 10 1/2 -> 52 x 52 x 64
12 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF
13 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF
14 route 13 12 -> 52 x 52 x 128
15 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
16 route 10 15 -> 52 x 52 x 256
17 max 2x 2/ 2 52 x 52 x 256 -> 26 x 26 x 256 0.001 BF
18 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
19 route 18 1/2 -> 26 x 26 x 128
20 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF
21 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF
22 route 21 20 -> 26 x 26 x 256
23 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
24 route 18 23 -> 26 x 26 x 512
25 max 2x 2/ 2 26 x 26 x 512 -> 13 x 13 x 512 0.000 BF
26 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
27 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
28 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
29 conv 18 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 18 0.003 BF
30 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
31 route 27 -> 13 x 13 x 256
32 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
33 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
34 route 33 23 -> 26 x 26 x 384
35 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
36 conv 18 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 18 0.006 BF
37 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 6.812
avg_outputs = 299663
Allocate additional workspace_size = 99.68 MB
Loading weights from yolov4.conv.137...
seen 64, trained: 0 K-images (0 Kilo-batches_64)
Done! Loaded 38 layers from weights-file
Learning Rate: 0.00261, Momentum: 0.9, Decay: 0.0005
Detection layer: 30 - type = 28
Detection layer: 37 - type = 28
Create 6 permanent cpu-threads
Cannot load image multiple_images/damage224.jpg

Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV

Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV

Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV
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Error in load_data_detection() - OpenCV

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Error in load_data_detection() - OpenCV
Cannot load image multiple_images/damage206.jpg

Error in load_data_detection() - OpenCV
Loaded: 0.279045 seconds
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@Shafayet-Shawal96
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Shafayet-Shawal96 commented Dec 21, 2021 •
edited
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I solved the problem by having the test.txt and train.txt files changed
from "multiple_images/laptop1.jpg"
to "data/multiple_images/laptop1.jpg"
because all my pictures were in the darknet/data/multiple_images folder in the drive.
@Shayendra, @sharoseali, @asdsdlgl

@tanvirbadsha
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I solved the problem by having the test.txt and train.txt files changed from "multiple_images/laptop1.jpg" to "data/multiple_images/laptop1.jpg" because all my pictures were in the darknet/data/multiple_images folder in the drive. @Shayendra, @sharoseali, @asdsdlgl

Thanks a lot brother. this solved my problem too. I had to do some other changes also because of directory issues. but this idea of your saved my life. Thanks again.

@DrAlkebsi
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Actually, this problem is caused due to wrong path. the training process is executed inside the darknet directory, while your images are inside a subdirectory darknet/data/'your_imgaes_folder_name' . To overcome this problem you need to make sure that you run the script that generats train.txt and test.txt in the darknet folder, and make sure that you write the right path to your images in the script that it can find the images. I will show you this in an example below:

I use a python script called process.py downloaded online from this github link. The script is shown below:
`import glob
import os
import numpy as np
import sys

current_dir = "multiple_images"
split_pct = 10;
file_train = open("train.txt", "w")
file_val = open("test.txt", "w")
counter = 1
index_test = round(100 / split_pct)
for pathAndFilename in glob.iglob(os.path.join(current_dir, "*.jpg")):
title, ext = os.path.splitext(os.path.basename(pathAndFilename))
if counter == index_test:
counter = 1
file_val.write(current_dir + "/" + title + '.jpg' + "\n")
else:
file_train.write(current_dir + "/" + title + '.jpg' + "\n")
counter = counter + 1
file_train.close()
file_val.close()`

  • change the current_dir = "multiple_images" to current_dir = "data/multiple_images", and replace multipe_images with the folder name that you have your images in.

  • save the script inside your darknet folder and run it

  • you will have two files generated train.txt and test.txt, copy these two files from darknet directory to the subdirectory darknet/data

  • if you open any of these two files you will noticed that the path of the images inside is being a full path from the base directory (darknet) where you run the training code. this will solve your problem.

  • the content of the train.txt and test.txt should look something like this
    data/'your_images_folder_name'/1.jpg
    data/'your_images_folder_name'/2.jpg
    data/'your_images_folder_name'/3.jpg
    data/'your_images_folder_name'/4.jpg

hope this explains well

@prudhvi-charan
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prudhvi-charan commented Mar 2, 2022 •
edited
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Hey, I got the same error. But everything in train.txt seems to be fine. The train.txt has paths like
data/obj/IMG_0237.jpg
data/obj/IMG_0245.jpg, etc.,. and test.txt has paths like data/test/IMG_1147.jpg.
I've manually created train.txt and test.txt files and made sure that all paths are existing and correct. But I still got the error. Does anyone know what to do?
image

The images for training exist at darknet/data/obj/
image

@prudhvi-charan
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I was able to resolve the issue by replacing .jpg in train.txt and test.txt with .JPG

@GabrielFerrante
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I use Ubuntu 20.04 with an RTX 3060
NVIDIA-SMI 470.103.01 Driver Version: 470.103.01 CUDA Version: 11.4

and the same problem also occurs

Error in load_data_detection() - OpenCV
Cannot load image data/labels/oncaParda00000401.txt

Error in load_data_detection() - OpenCV
Cannot load image data/labels/jaguarundi00000292.txt

Error in load_data_detection() - OpenCV
try to allocate additional workspace_size = 284.20 MB
CUDA allocate done!
Loaded: 0.000038 seconds
Segmentation fault (core dumped)

I checked my train.txt and everything is correct. I added the labels in the data/labels folder and the images in data/obj/ and I still get an error.

@vasanth4224
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@GabrielFerrante did you solve this error?

CUDA-version: 11020 (11060), cuDNN: 8.1.1, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.2.0
Prepare additional network for mAP calculation...
0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 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 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
3 route 1 -> 208 x 208 x 64
4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
9 route 8 2 -> 208 x 208 x 128
10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF
11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
13 route 11 -> 104 x 104 x 128
14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
22 route 21 12 -> 104 x 104 x 128
23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF
24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 route 24 -> 52 x 52 x 256
27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
53 route 52 25 -> 52 x 52 x 256
54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF
55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 route 55 -> 26 x 26 x 512
58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
84 route 83 56 -> 26 x 26 x 512
85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF
86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
88 route 86 -> 13 x 13 x1024
89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
103 route 102 87 -> 13 x 13 x1024
104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF
105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF
109 route 107 -> 13 x 13 x 512
110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF
111 route 107 -> 13 x 13 x 512
112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF
113 route 112 110 108 107 -> 13 x 13 x2048
114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF
115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
119 route 85 -> 26 x 26 x 512
120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
121 route 120 118 -> 26 x 26 x 512
122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
129 route 54 -> 52 x 52 x 256
130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
131 route 130 128 -> 52 x 52 x 256
132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 52 x 52 x 128
141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF
142 route 141 126 -> 26 x 26 x 512
143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 26 x 26 x 256
152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF
153 route 152 116 -> 13 x 13 x1024
154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 59.570
avg_outputs = 489910
Allocate additional workspace_size = 52.44 MB
yolov4-drone_
0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4
net.optimized_memory = 0
mini_batch = 4, batch = 64, 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 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
3 route 1 -> 208 x 208 x 64
4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
9 route 8 2 -> 208 x 208 x 128
10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF
11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
13 route 11 -> 104 x 104 x 128
14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
22 route 21 12 -> 104 x 104 x 128
23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF
24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 route 24 -> 52 x 52 x 256
27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
53 route 52 25 -> 52 x 52 x 256
54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF
55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 route 55 -> 26 x 26 x 512
58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
84 route 83 56 -> 26 x 26 x 512
85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF
86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
88 route 86 -> 13 x 13 x1024
89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
103 route 102 87 -> 13 x 13 x1024
104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF
105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF
109 route 107 -> 13 x 13 x 512
110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF
111 route 107 -> 13 x 13 x 512
112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF
113 route 112 110 108 107 -> 13 x 13 x2048
114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF
115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
119 route 85 -> 26 x 26 x 512
120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
121 route 120 118 -> 26 x 26 x 512
122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
129 route 54 -> 52 x 52 x 256
130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
131 route 130 128 -> 52 x 52 x 256
132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 52 x 52 x 128
141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF
142 route 141 126 -> 26 x 26 x 512
143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 26 x 26 x 256
152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF
153 route 152 116 -> 13 x 13 x1024
154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 59.570
avg_outputs = 489910
Allocate additional workspace_size = 149.82 MB
Loading weights from yolov4.conv.137...
seen 64, trained: 0 K-images (0 Kilo-batches_64)
Done! Loaded 137 layers from weights-file
Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
Resizing, random_coef = 1.40

608 x 608
Create 6 permanent cpu-threads
Cannot load image data/obj/Drone_frame_2295.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2040.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2503.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_3275.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2489.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_4312.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_7001.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_6594.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_1455.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_6646.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_5962.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_6570.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_8601.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_5265.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_7478.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_6526.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_3051.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_10562.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_1921.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_4712.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2307.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2533.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_912.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_9387.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_11705.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_1088.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_6748.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_10988.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2913.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_7241.jpg

Error in load_data_detection() - OpenCV
Cannot load image data/obj/Drone_frame_2063.jpg

Error in load_data_detection() - OpenCV

@HuyPham55
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HuyPham55 commented Jun 11, 2023 •
edited
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I solved this problem on Google Colab by using absolute path. Generally if you follow the direction, your path would look like something similar to following:

/content/darknet/data/obj/train_img.jpg

/content/darknet/data/test/test_img.jpg

@Ganesh9100
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Make sure 2 things,

  1. one is Path of the image
  2. your train.txt ( image path should not have any single or double quotes )

@shamindraparui shamindraparui mentioned this issue Jun 23, 2023
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@Chillyblast
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I use Ubuntu 20.04 with an RTX 3060 NVIDIA-SMI 470.103.01 Driver Version: 470.103.01 CUDA Version: 11.4

and the same problem also occurs

Error in load_data_detection() - OpenCV Cannot load image data/labels/oncaParda00000401.txt

Error in load_data_detection() - OpenCV Cannot load image data/labels/jaguarundi00000292.txt

Error in load_data_detection() - OpenCV try to allocate additional workspace_size = 284.20 MB CUDA allocate done! Loaded: 0.000038 seconds Segmentation fault (core dumped)

I checked my train.txt and everything is correct. I added the labels in the data/labels folder and the images in data/obj/ and I still get an error.

Hey did you solved it? I am having the same issue.

@Chillyblast
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Actually, this problem is caused due to wrong path. the training process is executed inside the darknet directory, while your images are inside a subdirectory darknet/data/'your_imgaes_folder_name' . To overcome this problem you need to make sure that you run the script that generats train.txt and test.txt in the darknet folder, and make sure that you write the right path to your images in the script that it can find the images. I will show you this in an example below:

I use a python script called process.py downloaded online from this github link. The script is shown below: `import glob import os import numpy as np import sys

current_dir = "multiple_images" split_pct = 10; file_train = open("train.txt", "w") file_val = open("test.txt", "w") counter = 1 index_test = round(100 / split_pct) for pathAndFilename in glob.iglob(os.path.join(current_dir, "*.jpg")): title, ext = os.path.splitext(os.path.basename(pathAndFilename)) if counter == index_test: counter = 1 file_val.write(current_dir + "/" + title + '.jpg' + "\n") else: file_train.write(current_dir + "/" + title + '.jpg' + "\n") counter = counter + 1 file_train.close() file_val.close()`

  • change the current_dir = "multiple_images" to current_dir = "data/multiple_images", and replace multipe_images with the folder name that you have your images in.
  • save the script inside your darknet folder and run it
  • you will have two files generated train.txt and test.txt, copy these two files from darknet directory to the subdirectory darknet/data
  • if you open any of these two files you will noticed that the path of the images inside is being a full path from the base directory (darknet) where you run the training code. this will solve your problem.
  • the content of the train.txt and test.txt should look something like this
    data/'your_images_folder_name'/1.jpg
    data/'your_images_folder_name'/2.jpg
    data/'your_images_folder_name'/3.jpg
    data/'your_images_folder_name'/4.jpg

hope this explains well

Hey I am using jetpack 4.6.1 and I have changed the directory just as you mentioned but still getting the same error even the files are in data/image format. The script I used is following beacause my dataset is already divided into 3 folders (train test split) and I have already copied them to darknet/data.

import os

Define the base directory where Darknet is located

base_dir = "/home/talix/darknet"

Define the directories containing the images

image_dirs = ["test", "train", "valid"]

Open files for writing train.txt, test.txt, and valid.txt file paths

file_train = open(os.path.join(base_dir, "train.txt"), "w")
file_test = open(os.path.join(base_dir, "test.txt"), "w")
file_valid = open(os.path.join(base_dir, "valid.txt"), "w")

Function to write file paths to the appropriate files

def write_file_paths(file_handle, file_paths):
for path in file_paths:
file_handle.write(path + "\n")

Loop through each image directory

for image_dir in image_dirs:
# Get all image files in the directory
image_files = os.listdir(os.path.join(base_dir, "data", image_dir))

# Write file paths to the appropriate files
for image_file in image_files:
    file_path = os.path.join("data", image_dir, image_file)
    if image_dir == "train":
        file_train.write(file_path + "\n")
    elif image_dir == "test":
        file_test.write(file_path + "\n")
    elif image_dir == "valid":
        file_valid.write(file_path + "\n")

Close all file handles

file_train.close()
file_test.close()
file_valid.close()

@GabrielFerrante
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@GabrielFerrante did you solve this error?

CUDA-version: 11020 (11060), cuDNN: 8.1.1, CUDNN_HALF=1, GPU count: 1 CUDNN_HALF=1 OpenCV version: 4.2.0 Prepare additional network for mAP calculation... 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 net.optimized_memory = 0 mini_batch = 1, batch = 16, time_steps = 1, train = 0 layer filters size/strd(dil) input output 0 Create CUDA-stream - 0 Create cudnn-handle 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 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 3 route 1 -> 208 x 208 x 64 4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF 8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 9 route 8 2 -> 208 x 208 x 128 10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF 11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 13 route 11 -> 104 x 104 x 128 14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 22 route 21 12 -> 104 x 104 x 128 23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF 24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 route 24 -> 52 x 52 x 256 27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 53 route 52 25 -> 52 x 52 x 256 54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF 55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 route 55 -> 26 x 26 x 512 58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 84 route 83 56 -> 26 x 26 x 512 85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF 86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 88 route 86 -> 13 x 13 x1024 89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 103 route 102 87 -> 13 x 13 x1024 104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF 105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF 109 route 107 -> 13 x 13 x 512 110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF 111 route 107 -> 13 x 13 x 512 112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF 113 route 112 110 108 107 -> 13 x 13 x2048 114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF 115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 119 route 85 -> 26 x 26 x 512 120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 121 route 120 118 -> 26 x 26 x 512 122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 129 route 54 -> 52 x 52 x 256 130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 131 route 130 128 -> 52 x 52 x 256 132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF 139 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 140 route 136 -> 52 x 52 x 128 141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF 142 route 141 126 -> 26 x 26 x 512 143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF 150 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 151 route 147 -> 26 x 26 x 256 152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF 153 route 152 116 -> 13 x 13 x1024 154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF 161 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 59.570 avg_outputs = 489910 Allocate additional workspace_size = 52.44 MB yolov4-drone_ 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 net.optimized_memory = 0 mini_batch = 4, batch = 64, 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 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 3 route 1 -> 208 x 208 x 64 4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF 8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 9 route 8 2 -> 208 x 208 x 128 10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF 11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 13 route 11 -> 104 x 104 x 128 14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 22 route 21 12 -> 104 x 104 x 128 23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF 24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 route 24 -> 52 x 52 x 256 27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 53 route 52 25 -> 52 x 52 x 256 54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF 55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 route 55 -> 26 x 26 x 512 58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 84 route 83 56 -> 26 x 26 x 512 85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF 86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 88 route 86 -> 13 x 13 x1024 89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 103 route 102 87 -> 13 x 13 x1024 104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF 105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF 109 route 107 -> 13 x 13 x 512 110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF 111 route 107 -> 13 x 13 x 512 112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF 113 route 112 110 108 107 -> 13 x 13 x2048 114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF 115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 119 route 85 -> 26 x 26 x 512 120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 121 route 120 118 -> 26 x 26 x 512 122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 129 route 54 -> 52 x 52 x 256 130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 131 route 130 128 -> 52 x 52 x 256 132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF 139 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 140 route 136 -> 52 x 52 x 128 141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF 142 route 141 126 -> 26 x 26 x 512 143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF 150 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 151 route 147 -> 26 x 26 x 256 152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF 153 route 152 116 -> 13 x 13 x1024 154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF 161 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 59.570 avg_outputs = 489910 Allocate additional workspace_size = 149.82 MB Loading weights from yolov4.conv.137... seen 64, trained: 0 K-images (0 Kilo-batches_64) Done! Loaded 137 layers from weights-file Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005 Detection layer: 139 - type = 28 Detection layer: 150 - type = 28 Detection layer: 161 - type = 28 Resizing, random_coef = 1.40

608 x 608 Create 6 permanent cpu-threads Cannot load image data/obj/Drone_frame_2295.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2040.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2503.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_3275.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2489.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_4312.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_7001.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_6594.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_1455.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_6646.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_5962.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_6570.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_8601.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_5265.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_7478.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_6526.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_3051.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_10562.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_1921.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_4712.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2307.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2533.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_912.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_9387.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_11705.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_1088.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_6748.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_10988.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2913.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_7241.jpg

Error in load_data_detection() - OpenCV Cannot load image data/obj/Drone_frame_2063.jpg

Error in load_data_detection() - OpenCV

I resolved it by noticing that some images were corrupted. So fixing them was the solution. Some times I had to change the image channel from RGB to BGR and it worked. But there were also images that I had to delete

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