Darknet: yolo v3 no detection with cuda=1 cudnn=1 and opencv=1
90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs
92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs
93 conv 255 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 255 0.177 BFLOPs
94 yolo
95 route 91
96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BFLOPs
97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BFLOPs
100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255 0.353 BFLOPs
106 yolo
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 0.027075 seconds.
init done
opengl support available
my yolo stops on this process.. i don no whats the problem
my makefile is like this
GPU=1
CUDNN=1
OPENCV=1
OPENMP=0
DEBUG=0
ARCH= -gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=[sm_50,compute_50] \
-gencode arch=compute_52,code=[sm_52,compute_52]\
-gencode arch=compute_61,code=[sm_61,compute_61]\
-gencode arch=compute_70,code=[sm_70,compute_70]
# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
and this is my nvidia-smi
| NVIDIA-SMI 410.79 Driver Version: 410.79 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2070 Off | 00000000:01:00.0 On | N/A |
| 0% 39C P8 4W / 175W | 678MiB / 7949MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 959 G /usr/lib/xorg/Xorg 566MiB |
| 0 2421 G compiz 111MiB |
+-----------------------------------------------------------------------------+
when i try to execute this command under
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
it doesn't make any bounding box.. and i also tried -thresh 0.01 but it stops at at opengl support available
and nothing is happend..
pls help me T.T
yujiny97
All 16 comments
Does detection work if you build with GPU=0, CUDNN=0?
What about with GPU=1, CUDNN=0
LukeAI
on 16 Feb 2019
Does detection work if you build with GPU=0, CUDNN=0?
What about with GPU=1, CUDNN=0
I am having the same issue.
Objects are not being detected using GPU = 1, CUDNN=1.Neither are they shown in shown in terminal ,nor bounding boxes are drawn around the objects in the predictions.jpg image.
The program is running perfectly fine when compiled with GPU = 1 and CUDNN = 0 and is showing bounding boxes in predictions.jpg.
I don't have openCV so I've compiled using OPENCV=0
abhishekbaghel
on 16 Feb 2019
@abhishekbaghel have you verified your CuDNN install as described here? https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#verify
LukeAI
on 16 Feb 2019
@LukeAI Yes I verified it right now.. CuDNN seems to be fine.. still no predictions.
Just realised YOLOv3-tiny is working fine.
Yolov3-spp is working too.. with similar fps and accuracy.
abhishekbaghel
on 16 Feb 2019
Looks like an old bug.. https://github.com/pjreddie/darknet/issues/1220#issue-375943911
abhishekbaghel
on 16 Feb 2019
@LukeAI @abhishekbaghel I tried it with GPU=1 CUDNN=0 OPENCV=1 and others =0 and it shows
darknet: ./src/cuda.c:36: check_error: Assertion 0' failed.
Aborted (core dumped)
like this. what is for OPENMP?? do this need any download??
I also tried yolov3-tiny but it doesn't work as well
yujiny97
on 17 Feb 2019
@numuhwana try the following mandatory post installation steps of CUDA before compiling darknet https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions
See if it works.
abhishekbaghel
on 17 Feb 2019
@abhishekbaghel thank you so much for helping me! i got the solution, the problem was that version of cuda was not matched with GPU. my cuda's version was 9.0, so it didn't fit in RTX 2070.
yujiny97
on 17 Feb 2019
@abhishekbaghel thank you so much for helping me! i got the solution, the problem was that version of cuda was not matched with GPU. my cuda's version was 9.0, so it didn't fit in RTX 2070.
I meet the same problem. Using Cuda 10.0 CUDNN 7.4 and set CUDNN=0 in makefile works fine
xvolica
on 20 Feb 2019
@xvolica
I changed makefile a bit.
did you add -gencode arch=compute_75,code=[sm_75,compute_75] in the makefile for ARCH?
if this doesn't work just try
width=416
height=416
in yolov3.cfg file.
for CUDNN check if cudnn.h in the /usr/local/cuda/include
and check libcudnn* in /usr/local/cuda/lib64
yujiny97
on 21 Feb 2019
Guys, I have almost similar issue.
CUDA - 9.0
cuDNN - 7.0.5 (I couldn't verify mnistCUDNN) I checked here - link
Nvidia - Geforce 840m
I tested on my web cam (GPU=1, CUDNN=0, OPENCV=1) it detects objects, but speed is very slow(5 fps)
Since I think CUDNN installed (GPU=1, CUDNN=1, OPENCV=1) doesn't show anything, speed slower.
Do you have any idea how to compile GPU, CUDNN, OPENCV?
Bncer
on 11 Mar 2019
@LukeAI @abhishekbaghel @numuhwana
Information in nvidia-smi https://github.com/pjreddie/darknet/issues/1435#issue-411028604 shows usage of GPU. In his case
0 2421 G compiz 111MiB
Am I right?
Bncer
on 12 Mar 2019
I have set all variables to zero in my Makefile, but I still can't get detections on the picture, my Makefile is like that:
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0
and running command as follow:
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
my project directory is like this:
after running that command under this directory, there is only predictions.jpg added in this directory, and no detections on it.
Can anyone tell me what I do wrong?
David3310273
on 17 Mar 2019
firstly, i run the commands from https://pjreddie.com/darknet/yolo/：
git clone https://github.com/pjreddie/darknet
cd darknet
make
then, if i use yolov3，run:
CUDA_VISIBLE_DEVICES='7' ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
there is nothing, even i add "-thresh 0.03 ".
then, i donot change anything, use yolov2:
CUDA_VISIBLE_DEVICES='7' ./darknet detect cfg/yolov2.cfg yolov2.weights data/dog.jpg
it work!
also, follow the idea
- use setting "GPU=1 CUDNN=0"
- make clean && make
now, yolov3 work.
ChanChiChoi
on 25 Mar 2019
@abhishekbaghel thank you so much for helping me! i got the solution, the problem was that version of cuda was not matched with GPU. my cuda's version was 9.0, so it didn't fit in RTX 2070.
I meet the same problem. Using Cuda 10.0 CUDNN 7.4 and set CUDNN=0 in makefile works fine
Upgrade to cuda10 and problem solved! Thank you, you saved my time~
jxmelody
on 15 May 2019
I have set all variables to zero in my Makefile, but I still can't get detections on the picture, my Makefile is like that:
GPU=0 CUDNN=0 OPENCV=0 OPENMP=0 DEBUG=0and running command as follow:
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpgmy project directory is like this:
after running that command under this directory, there is only predictions.jpg added in this directory, and no detections on it.
Can anyone tell me what I do wrong?
maybe you should make first
long123long
on 28 Sep 2019
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