Trained model gives zero detections

[bonzoq]

I compiled your version of darknet on Amazon Linux (using Tesla K80 GPU) with the following settings:

GPU=1
CUDNN=1
OPENCV=1
DEBUG=0
OPENMP=0
LIBSO=0

...

ARCH=  -gencode arch=compute_37,code=sm_37

I trained Yolo for 300 iterations using the steps described in https://pjreddie.com/darknet/yolo/ .

When I try to perform detection on a single image:

./darknet detector test cfg/voc.data cfg/yolo-voc.cfg backupVOC/yolo-voc_300.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg

I get no detections. I think I should get some (though not correct detections) even though the training was limited.

On another occasion I trained a classifier reproducing the steps from this tutorial https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/ . Training went on for a few thousand iterations with the average lost as low as 0.05 in the end and yet still I was unable to detect anything with that model.

What might I be doing wrong ?

[TheMikeyR]

You should recompile with OpenCV=0 then the result will be saved as an image in the root folder of darknet. The OpenCV is only used to show detections in a window and since you are using the AWS I assume you are doing it through an ssh connection.

[AlexeyAB]

@bonzoq Results of successfully training on Windows and Linux: https://github.com/AlexeyAB/darknet/issues/243#issuecomment-340908751

Can you get any bounded boxes using this command? ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg backupVOC/yolo-voc_300.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg -thresh 0.01

[bonzoq]

Thanks for your quick responses @TheMikeyR and @AlexeyAB.

I recompiled with OpenCV=0 and even with the threshold set to 0.01 I get no detections.

Threshold 0.00 also gives no detections. When I use the original yolo model I get plenty of detections.

[sc1234qwer]

I have the same problem. But training with tiny-yolo-voc.cfg is ok.

[AlexeyAB]

@bonzoq

When I use the original yolo model I get plenty of detections.

• Do you use yolo-voc_300.weights trained on my fork, to test on original fork, and it gives plenty of detections?

• Do you use batch=64 and subdivison=8 in your yolo-voc.cfg file for training?

• Try to update code from the last commit, and try to train again using in your cfg-file such params:

  learning_rate=0.0001
  steps=100,25000,35000
  scales=10,.1,.1

[bonzoq]

@AlexeyAB

• When I use your fork to perform detections on this one image I mentioned above (VOCdevkit/VOC2007/JPEGImages/009460.jpg) with the model https://pjreddie.com/media/files/yolo.weights I get plenty of detections. yolo-voc_300.weights I trained on your fork. When I try to use it to perform detections on the same image I get zero detections.

• Yes, I use batch=64 and subdivison=8 in my yolo-voc.cfg file for training.

• I will try to retrain using your last commit and updated cfg file.

Thanks, I will let you know!

[AlexeyAB]

@bonzoq

When I use your fork to perform detections on this one image I mentioned above (VOCdevkit/VOC2007/JPEGImages/009460.jpg) with the model https://pjreddie.com/media/files/yolo.weights I get plenty of detections.

• yolo.weights is COCO-model and it should be used for yolo.cfg only, so check that you use it in a such way: ./darknet detector test cfg/coco.data cfg/yolo.cfg yolo.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg

• To use VOC-model, download yolo-voc.weights and use it such: ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg

• Also if you train your model on VOC-dataset, usually you can see any detections after >1000 iterations with low threshold -thresh 0.01

• And to train your own model, try to use such params: https://github.com/AlexeyAB/darknet/issues/243#issuecomment-339659709

[bonzoq]

@AlexeyAB thanks for pointing out I was using a COCO model with VOC config.

Also if you train your model on VOC-dataset, usually you can see any detections after >1000 iterations with low threshold -thresh 0.01

Does it mean that when training on VOC-dataset I won't get any detections with -thresh 0.01 and fewer than 1000 iterations?

[phqmourao]

Hi, I'm having the same problem. I think something is wrong with generated weights files after commit ae74d0ef31485f84e1856b4733135d2753dbb033. Weights files created before are ok.

I use VS 2017, Cuda 9.0, cuDNN 7, OpenCV 3.2.0 cfg: yolo-voc.2.0.cfg and resnet50_yolo.cfg

[AlexeyAB]

@phqmourao Thank you, I fixed it.

@bonzoq @TheMikeyR @sc1234qwer An error was added 5 days ago. Now I have fixed it.

[bonzoq]

So I compiled the code with the latest commit Fixed bug with: net->seen and trained the network with default parameters. After 1000 iterations I get zero detections even with threshold 0.001.

Maybe the problem lays somewhere else? How can I debug?

[AlexeyAB]

@bonzoq

• What command line do you use to train?
• Can you detect anything using standard model using this fork? /darknet detector test cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg
• Also can you provide your cfg and weights files and image on which you try to detect objects?

---

This is strange, I just train my own model with 6 objects based on yolo-voc.2.0.cfg using 400 iterations on Windows 7x64 (CUDA 8.0, cuDNN 6.0, OpenCV 3.3.0), and it can detect objects.

• train: darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23
• detect: darknet.exe detector test data/obj.data yolo-obj.cfg backup/yolo-obj_400.weights img_4_109.jpg

[bonzoq]

@AlexeyAB I will reply tomorrow as I don't have access to the server right now. Thank you for your prompt reply. Greatly appreciated.

[bonzoq]

@AlexeyAB

./darknet detector test cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg

gives me no detections whatsoever.

1. I downloaded yolo-voc.weights from https://pjreddie.com/media/files/yolo-voc.weights
2. VOC.data:

   train  = VOCdevkit/train.txt
   valid  = VOCdevkit/2007_test.txt
   names = data/voc.names
   backup = backupVOC2

3. yolo-voc.cfg:

   
   [net]
   # Testing
   #batch=1
   #subdivisions=1
   # Training
   batch=64
   subdivisions=16
   height=416
   width=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 = 80200 policy=steps steps=40000,60000 scales=.1,.1

[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

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

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

[maxpool] size=2 stride=2

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

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

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

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=512 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] 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

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

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

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

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

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

#######

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[route] layers=-9

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

[reorg] stride=2

[route] layers=-1,-4

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=125 activation=linear

[region] anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 bias_match=1 classes=20 coords=4 num=5 softmax=1 jitter=.3 rescore=1

object_scale=5 noobject_scale=1 class_scale=1 coord_scale=1

absolute=1 thresh = .6 random=1

4. `VOCdevkit/VOC2007/JPEGImages/009460.jpg`

![009460](https://user-images.githubusercontent.com/2465884/32101377-3fd82452-bb18-11e7-8ee3-58b135a5bdc3.jpg)

[AlexeyAB]

@bonzoq Try to use this command with -i 0 at the end: ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg -i 0

And if it doesn't work show screenshot of your console output.

Just now I try it on both Windows 7x64 and Linux x64 (Debian 8):

[bonzoq]

@AlexeyAB sadly still no luck:

I updated the my previous comment to include the whole content of yolo-voc.cfg, which it didn't before.

[AlexeyAB]

@bonzoq I found the solution - set batch=1 subdivision=1 for detection.

---

In general

On Linux:

• for training you must set batch=64 subdivision=16 (or subdivision=8)
• for detection you must set batch=1 subdivision=1

On Windows:

• for training you must set batch=64 subdivision=16 (or subdivision=8)
• for detection you can set any values: batch=1 subdivision=1 or batch=64 subdivision=16

I'll look for a reason why we must set the values (batch=1 subdivision=1) for detection on Linux.

[bonzoq]

@AlexeyAB I set batch=1 and subdivision=1.

Sadly it didn't help. Can I debug this somehow?

[AlexeyAB]

@bonzoq

1. Also try to build without CUDA, and do: ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights VOCdevkit/VOC2007/JPEGImages/009460.jpg -i 0

GPU=0
CUDNN=0
OPENCV=0
DEBUG=0
OPENMP=0
LIBSO=0

---

2. Try to build with LIBSO=1, put yolo-voc.cfg and yolo-voc.weights near with compiled uselib file and do: ./uselib VOCdevkit/VOC2007/JPEGImages/009460.jpg

[scamianbas]

Hi all, I have sort of the same issue than @bonzoq here. The training looks ok, it converges to an avg 0.06, high IOU, Recall 1.0 almost everywhere, but on detection it always draws a box around the whole image even there's nothing to detect. :/

[bonzoq]

@AlexeyAB

1. I built with those settings to no avail.
2. I built with LIBSO=1 and tried but that did not work:

[zhang11wu4]

@AlexeyAB I encounter the same question in windows10, could you help to fix the bug? I downloaded the stop and yield JPEG format images from http://guanghan.info/blog/en/my-works/train-yolo/, Merged them together, the image number only is 554. I trained the images following the instruction “How to train (to detect your custom objects):”, cmd is “darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23”,after 3000 iterations get the following result, Avg recall = 1.000, avg loss = 0.103, but I can’t detect any objects with cmd”arknet.exe detector test data/obj.data yolo-obj.cfg backup/yolo-obj_3000.weights”. Following is the training processing screenshot.

Following is the obj.data file content: classes= 2 train = data/train.txt valid = data/train.txt names = data/obj.names backup = backup/ Following is the yolo-obj.cfg file content: _[net] batch=64 subdivisions=8 height=416 width=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1

learning_rate=0.0001 max_batches = 45000 policy=steps steps=100,25000,35000 scales=10,.1,.1

[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

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

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

[maxpool] size=2 stride=2

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

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

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

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=512 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] 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

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

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

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

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

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

#######

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[route] layers=-9

[reorg] stride=2

[route] layers=-1,-3

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=35 activation=linear

[region] anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 bias_match=1 classes=2 coords=4 num=5 softmax=1 jitter=.2 rescore=1

object_scale=5 noobject_scale=1 class_scale=1 coord_scale=1

absolute=1 thresh = .6 random=0_

Following is the detection cmd and result screenshot.

[AlexeyAB]

@zhang11wu4 Can you detect anything using default trained model yolo-voc.cfg and yolo-voc.weights downloaded by the link? https://pjreddie.com/darknet/yolo/

[zhang11wu4]

@AlexeyAB yes, I can detect objects correctly with "yolo-voc.cfg" and "yolo-voc.weights" downloaded from that website.

[AlexeyAB]

@zhang11wu4 Can you share your yolo-obj_3000.weights using GoogleDisk?

[zhang11wu4]

@AlexeyAB https://drive.google.com/file/d/0Bw2dL4mXINuzMVBUaGFlamk0WUU/view?usp=sharing pls check, thanks!

[AlexeyAB]

@zhang11wu4 I can't detect anything too using this weights-file.

• What commit do you use - when did you clone the repository?
• Did you use this fork on Windows 7/10 or Linux?
• What version of MSVS, CUDA, cuDNN, OpenCV do you use?
• Can you share your train.txt and all training dataset (images and labels) in one archive?

[zhang11wu4]

@AlexeyAB

1. Commit maybe b714004546b97e9a43fae3e385dbefb56cecafb6 [b714004], I just downloaded the zip file. 2.My operation system is Win10 with cuda8.0, cudnn5.1,opencv3.2,vs2015. 3.I have compress all project files(including train.txt, images and others) into a rar file to google disk: https://drive.google.com/open?id=0Bw2dL4mXINuzaVllaklkMHNDYzg Pls check, thanks a lot!

[AlexeyAB]

@zhang11wu4 I just trained model of 400 iterations using your dataset without any changes by using this command: darknet.exe detector train stopsign/obj.data stopsign/yolo-obj.cfg darknet19_448.conv.23

• I used MSVS 2015, CUDA 8.0, cuDNN v6 (for CUDA 8.0), OpenCV 3.3.0, Windows 7 x64. On GeForce GTX 970 (4 GB RAM) Maxwell - Compute capability (CC) 5.2
• I set batch=64 subdivision=8 in the yolo-obj.cfg from your darknet-master_guanghan_info_release.rar
• obj.data:

  classes=2
  train =stopsign/train.txt
  valid =stopsign/test.txt
  names=stopsign/obj.names
  backup=backup/

• train.txt didn't changed, and didn't changed images and txt-labels. Images placed:
  • C:\zjg\work\dev\darknet-master_guanghan_info\build\darknet\x64\data\obj\stopsign
  • C:\zjg\work\dev\darknet-master_guanghan_info\build\darknet\x64\data\obj\yieldsign

And I can detect stop sign by using this command: darknet.exe detector test stopsign/obj.data stopsign/yolo-obj.cfg backup/yolo-obj_400.weights stopsign/014.jpg -thresh 0.1

[AlexeyAB]

@zhang11wu4

• Do you use cudnn5.1 for CUDA 8.0, not for CUDA 7.5? https://developer.nvidia.com/rdp/cudnn-download
• What GPU do you use?
• Try to train without cuDNN open \darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove: CUDNN;

[zhang11wu4]

@AlexeyAB 1.Yes, I use CUDA8.0 with cudnn5.1; 2.GPU is GForce 980M; 3.I tried commit 6ccb418, it works, I can detect objects successfully with 4000 iterations with original dataset including stop signs and yield signs.
Train cmd: darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 Test cmd: darknet.exe detector test data/obj.data yolo-obj.cfg backup/yolo-obj_4000.weights

[scamianbas]

Hi @AlexeyAB , @zhang11wu4 , @bonzoq here's my conf :

• Ubuntu 14.04.5 LTS
• Nvidia GeForce GTX 760 4GB
• Nvidia Driver Version 375.39
• CUDA Version 8.0.61
• CUDNN 6.0.21

I used the tarball provided by @zhang11wu4 and enabled GPU, CUDNN, OPENCV and OPENMP in the Makefile then "make" it.

Then in "build/darknet/x64" I edited data/obj.data like this:

classes=2 train =data/train.txt valid =data/test.txt names=data/obj.names backup=backup/ because train.txt, test.txt and obj.names were located there. (there is no directory called stopsign)

I also had to rebuild train.txt and test.txt to have the paths in linux format and relative to current directory:

root@0e5c13ae42c1:~/WilliamZhang/build/darknet/x64# head data/train.txt data/obj/stopsign/001.JPEG data/obj/stopsign/002.JPEG data/obj/stopsign/003.JPEG

I did'nt change anything else.

Then I ran the following command: "/root/WilliamZhang/darknet detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23"

After 400 iterations, I ran the following command: "/root/WilliamZhang/darknet detector test data/obj.data yolo-obj.cfg backup/yolo-obj_400.weights data/obj/stopsign/014.JPEG -thresh 0.1"

The result is no detections.

I also tried with smaller thresholds (down to zero!) and even by setting batch and subdivisions to 1 in yolo-obj.cfg , again no detections.

I really don't understand what's going on ...

Maybe it's a linux specific issue when generating weights because when I try weights from the official website for instance the detection works very well, the problem it's when try to generate it by myself, maybe you can share your "yolo-obj_4000.weights" so I can give it a try ?

Thank you very much for your help

Mirko

[AlexeyAB]

@scamianbas

1. Try to update your code from the last commit.

2. Check that you edit the same obj.data that used in training command line

Then in "build/darknet/x64" I edited data/obj.data like this:

Then I ran the following command: "/root/WilliamZhang/darknet detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23"

3. Later I'll check training using last commit on Linux, but earlier it worked fine.

[zhang11wu4]

@scamianbas this the weight file https://drive.google.com/open?id=0Bw2dL4mXINuzS2hqOVRvaWZkMzA following is my cmd and detection result screenshot:

[scamianbas]

@AlexeyAB

1. I used commit 6ccb418 (same as @zhang11wu4) which is the latest as far as I know
2. I'm sure that I used the same obj.data, there's only one in my working directory (se below)
3. cool, I can't wait ! :+1:

@zhang11wu4 Thanks for your weights file, the detection worked fine ! (with batch=1 subdivisions=1)

It really looks like the issue is linux related...

[sc1234qwer]

one class with 100 images and after about 400--800 iterations,the weights work well, i am doubtful, the train so fast ??

[AlexeyAB]

@scamianbas

1. So I successfully trained model on Windows: https://github.com/AlexeyAB/darknet/issues/243#issuecomment-340512559
2. And successfully trained model on Linux x64 Debian 8: source code, executable file darknet, training dataset /data/obj, two trained models in /backup folder - you can download all by the url: https://drive.google.com/open?id=0BwRgzHpNbsWBZi0xVENMbm1ZMVk
   • unpack darknet_port.zip
   • try to run img_sign.sh
   • if it doesn't work, then recompile executable file, do: make clean and make -j8 in folder with Makefile
   • run img_sign.sh to check detection work
   • run valid_sign.sh to check recall work
   • run train_sign.sh to check training, and after 100 iterations, cancel training CTRL+C and check detection - run img_sign.sh

100 iterations too few for good prediction, but it enough to detect anything. For good prediction it should be trained ~4000 iterations for 2 classes (stop, yield) = 2000 per class

[scamianbas]

@AlexeyAB Yep it works ! Thank you very much ! Tomorrow I will analyze why and keep you informed. I had to recompile, dos2unix the .sh files and invoke bash to xecute them. Thanks a lot again !

[scamianbas]

@AlexeyAB please find below the src differences with @zhang11wu4 version, if you want analyze it:

root@0e5c13ae42c1:~/darknet_port# diff -rq src /root/WilliamZhang/src
Files src/demo.c and /root/WilliamZhang/src/demo.c differ
Files src/detector.c and /root/WilliamZhang/src/detector.c differ
Files src/image.c and /root/WilliamZhang/src/image.c differ
Files src/network.h and /root/WilliamZhang/src/network.h differ
Files src/parser.c and /root/WilliamZhang/src/parser.c differ
Files src/parser.h and /root/WilliamZhang/src/parser.h differ
Files src/yolo_console_dll.cpp and /root/WilliamZhang/src/yolo_console_dll.cpp differ
Files src/yolo_v2_class.cpp and /root/WilliamZhang/src/yolo_v2_class.cpp differ

root@0e5c13ae42c1:~/darknet_port# diff src/demo.c /root/WilliamZhang/src/demo.c
128c128
<     net = parse_network_cfg_custom(cfgfile, 1);
---
>     net = parse_network_cfg(cfgfile);

root@0e5c13ae42c1:~/darknet_port# diff src/detector.c /root/WilliamZhang/src/detector.c
14d13
< 
17,18c16
< #define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)""CVAUX_STR(CV_VERSION_REVISION)
< #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
---
> #pragma comment(lib, "opencv_world320.lib")  
20,23c18,20
< #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)
< #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
< #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
< #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
---
> #pragma comment(lib, "opencv_core2413.lib")  
> #pragma comment(lib, "opencv_imgproc2413.lib")  
> #pragma comment(lib, "opencv_highgui2413.lib") 
25d21
< 
27d22
< 
31a27
>   printf("detector.c train_detector()\n");
33a30
>   printf("detector.c train_images=%s\n", train_images);
35c32
< 
---
>   printf("detector.c backup_directory=%s\n", backup_directory);
37a35
>   printf("detector.c basefile=%s\n", base);
461a460,461
>   printf("detector.c test_detector() names0=%s\n", names[0]);
>   printf("detector.c test_detector() names1=%s\n", names[1]);
464c464
<     network net = parse_network_cfg_custom(cfgfile, 1);
---
>     network net = parse_network_cfg(cfgfile);
478d477
<           if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
565d563
<       if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;

root@0e5c13ae42c1:~/darknet_port# diff src/image.c /root/WilliamZhang/src/image.c  
19d18
< #include "opencv2/imgcodecs/imgcodecs_c.h"
188c187
< 
---
>   //printf("draw_detections()_num=%d\n", num);
190a190
>       //printf("draw_detections()_class=%d\n", class);
191a192
>       //printf("draw_detections()_prob=%f\n", prob);
194,201d194
<           //// for comparison with OpenCV version of DNN Darknet Yolo v2
<           //printf("\n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h);
<           // int k;
<           //for (k = 0; k < classes; ++k) {
<           //  printf("%f, ", probs[i][k]);
<           //}
<           //printf("\n");
< 
301c294
<           //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class, names[class]);
---
> 
1368,1369c1361,1362
<   //image out = load_image_stb(filename, c);  // OpenCV 3.x
<   image out = load_image_cv(filename, c);
---
>   image out = load_image_stb(filename, c);    // OpenCV 3.x
>   //image out = load_image_cv(filename, c);

root@0e5c13ae42c1:~/darknet_port# diff src/network.h /root/WilliamZhang/src/network.h
24c24
<   int *seen;
---
>   uint64_t *seen;

root@0e5c13ae42c1:~/darknet_port# diff src/parser.c /root/WilliamZhang/src/parser.c
587,591d586
<   return parse_network_cfg_custom(filename, 0);
< }
< 
< network parse_network_cfg_custom(char *filename, int batch)
< {
608d602
<   if (batch > 0) net.batch = batch;
708,709d701
< 
< 
847c839
<     fwrite(net.seen, sizeof(int), 1, fp);
---
>     fwrite(net.seen, sizeof(uint64_t), 1, fp);
1036,1038c1028
<       uint64_t iseen = 0;
<       fread(&iseen, sizeof(uint64_t), 1, fp);
<       *net->seen = iseen;
---
>       fread(net->seen, sizeof(uint64_t), 1, fp);
1041c1031,1033
<       fread(net->seen, sizeof(int), 1, fp);
---
>       int iseen = 0;
>       fread(&iseen, sizeof(int), 1, fp);
>       *net->seen = iseen;

root@0e5c13ae42c1:~/darknet_port# diff src/parser.h /root/WilliamZhang/src/parser.h
6d5
< network parse_network_cfg_custom(char *filename, int batch);

root@0e5c13ae42c1:~/darknet_port# diff src/yolo_console_dll.cpp /root/WilliamZhang/src/yolo_console_dll.cpp
88c88
<   Detector detector("cfg/yolo-voc.cfg", "yolo-voc.weights");
---
>   Detector detector("yolo-voc.cfg", "yolo-voc.weights");

root@0e5c13ae42c1:~/darknet_port# diff src/yolo_v2_class.cpp /root/WilliamZhang/src/yolo_v2_class.cpp
57c57
<   net = parse_network_cfg_custom(cfgfile, 1);
---
>   net = parse_network_cfg(cfgfile);

[Chanki8658]

@AlexeyAB @bonzoq @scamianbas hello , i am using windows 10 , cuda 9.0 and Cudacnn 7.0 . and i followed all the steps mentioned in the blog to train the model https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/ and successfully trained the model . but when i am predicting using cmd PS C:\darknet\darknet\build\darknet\x64> .\darknet.exe detector test cfg\obj.data cfg\yolo-obj.cfg backup\yolo-obj_1000.weights C:\darknet\darknet\build\darknet\x64\NFPAdatas et\pos-1.jpg i ma not getting any detection . even i tried with yolo-9000 weights and label still its not detecting anything .

[Chanki8658]

obj.zip @bonzoq @AlexeyAB @scamianbas my config file are attached in zip file

[AlexeyAB]

root@0e5c13ae42c1:~/darknet_port# diff src/network.h /root/WilliamZhang/src/network.h
24c24
<   int *seen;
---
>   uint64_t *seen;

@scamianbas Yes, definitely @zhang11wu4 code version without bug fixes, i.e. one of commit between these two:

• https://github.com/AlexeyAB/darknet/commit/ae74d0ef31485f84e1856b4733135d2753dbb033#diff-3d6e69dd5b0d5232fc0779ad2cea1e00R24 commit with bug >= used commit
• https://github.com/AlexeyAB/darknet/commit/b714004546b97e9a43fae3e385dbefb56cecafb6#diff-3d6e69dd5b0d5232fc0779ad2cea1e00R24 commit with bug fix < used commit

@Chanki8658 Try to update your code from this repo.

[Chanki8658]

@AlexeyAB changes of b714004#diff-3d6e69dd5b0d5232fc0779ad2cea1e00R24 commit with bug fix < used commit this branch is already in my code which i built . How can i debug this . when i debug the code everything seems to be fine and just that float *probs = calloc(l.wl.hl.n, sizeof(float )); probability is always coming zero . could you please suggest

[Chanki8658]

@AlexeyAB parser.zip parser.c and network.c network.zip is attached

[Chanki8658]

@AlexeyAB could you suggest the last correct branch which built and predict successfully on windows .

[Chanki8658]

@AlexeyAB i also tried with pre-train weights . but still its not showing up anything https://drive.google.com/open?id=0B6yBEDUqsu7Ta1M1dE40OFV1Tms

[AlexeyAB]

@Chanki8658 You should do each step described here: https://github.com/AlexeyAB/darknet/issues/243#issuecomment-340908751

[TheMikeyR]

I saw the "help wanted"-label have been applied to this thread. I've completed the steps in https://github.com/AlexeyAB/darknet/issues/243#issuecomment-340908751 (had to make again for it to work) and I have no issues, detecting everything fine also after a new training.

./data/obj/stopsign/001.JPEG: Predicted in 0.067797 seconds.
: 73%
: 57%

System Info GPU: Quadro M1200 OS: Ubuntu 16.04 CUDA: 8.0 CUDNN: 5.1

[scamianbas]

Hi @AlexeyAB what is the "-i 0" option for ? Thanks

[AlexeyAB]

@scamianbas For using GPU number 0.