I've only figured out how to run detections on one image at a time, typing a new image filename each time. Is there any build-in way to run on an entire folder of images and save images with predictions to another folder?

Hi,

I'm trying to forward the feed of a webcam to YOLO in python. My problem is that I capture the images from OpenCV but i don't know how to transform them to an IMAGE object in order to perform the .network_detect() Could you please throw some insight on this matter?

Thank you!

I'm running cuda8.0 on Ubuntu16.04LTS.

root@29c797bc416c:~/darknet# ./darknet rnn train cfg/rnn.train.cfg -file ./t8.shakespeare.txt 
rnn
layer     filters    size              input                output
    0 RNN Layer: 256 inputs, 1024 outputs
		CUDA Error: out of memory
darknet: ./src/cuda.c:36: check_error: Assertion `0' failed.
Aborted (core dumped)
root@29c797bc416c:~/darknet# nvidia-smi
Fri Jul 21 14:11:16 2017       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.57                 Driver Version: 367.57                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K520           Off  | 0000:00:03.0     Off |                  N/A |
| N/A   62C    P0   104W / 125W |   3554MiB /  4036MiB |     92%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

Started playing with your project after catching the ted talk, very cool project. I've gotten it to compile with GPU=0, however When setting GPU=1 I get the following error.

/usr/include/string.h: In function ‘void* __mempcpy_inline(void*, const void*, size_t)’:
/usr/include/string.h:652:42: error: ‘memcpy’ was not declared in this scope
   return (char *) memcpy (__dest, __src, __n) + __n;
                                          ^
compilation terminated due to -Wfatal-errors.
Makefile:88: recipe for target 'obj/convolutional_kernels.o' failed
make: *** [obj/convolutional_kernels.o] Error 1

I'll keep tinkering and see if I can't figure it out just thought it might be worth reporting.

rico@deadsecuritylab:~/Desktop/darknet$ make g++ -Iinclude/ -Isrc/ -DOPENCV pkg-config --cflags opencv -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC -Ofast -DOPENCV -c ./src/image_opencv.cpp -o obj/image_opencv.o Package opencv was not found in the pkg-config search path. Perhaps you should add the directory containing `opencv.pc' to the PKG_CONFIG_PATH environment variable No package 'opencv' found ./src/image_opencv.cpp:5:10: fatal error: opencv2/opencv.hpp: No such file or directory 5 | #include "opencv2/opencv.hpp" | ^~~~~~~~~~~~~~~~~~~~ compilation terminated. make: *** [Makefile:86: obj/image_opencv.o] Error 1

Hey, I'm trying to train a classifier model in Darknet with my own dataset, consists of 4 class data. Though i found a cifar classification example in darknet, but i am unable to find in what format i should provide my data (renaming is required i guess, with classname at end?). or just tell me how should i proceed farther to use my dataset for a classifier specifying the classes that i want. I have followed this https://pjreddie.com/darknet/train-cifar/ link but still not clear how the classifier finds the class information. Please help me in this regards. Thanks in advance.

I am fairly new in using YOLO. I was wondering which function could I call to save an image from the darknet.py file found in /python directory. I would like to specify a manual location to save the detected image(containing detected markings).

I have looked at various function in .c files, but was unable to pinpoint the respective function. Although, I tried calling save_image(im) but I need to pass the detected image.

Any suggestions would be of great help!

Hello,

I am training YOLO on the COCO dataset and I have noticed that the weights only save in increments of 100 until iteration 900. After iteration 900, the next set of saved weights is at iteration 10,000, then 20,000 after that.

Is there a way to change how often weights are saved to $DARKNET_ROOT/backup? I would request there be a variable in cfg/yolo.cfg that allows the user to choose how often weights are saved to backup.

I am trying to train yolo on my custom dataset. However, the final predictions.jpg file is always empty for me.

• There is file in my root folder called bad.list, that contains thousands of entries such as:

data/labels/32_0.png data/labels/33_0.png data/labels/34_0.png data/labels/35_0.png ...

• At the time of prediction, I get similar errors:

Cannot load image "data/labels/32_0.png" Cannot load image "data/labels/33_0.png" Cannot load image "data/labels/34_0.png" Cannot load image "data/labels/35_0.png" Cannot load image "data/labels/36_0.png" ...

These png images are not part of my training dataset and do not exist in any configuration file. I'm guessing this is might be the reason why my predictions are empty with no bounding box around the desired object(?).

Could anybody please throw some light on this.

Can darknet detector demo use multiple GPUs?

-gpus doesn't seem to kick off a 2nd process on the 2nd GPU.

$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights Delhi.mkv -gpus 0,1

...

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.46                 Driver Version: 390.46                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla M60           Off  | 00003DB5:00:00.0 Off |                  Off |
| N/A   54C    P0   135W / 150W |   1450MiB /  8129MiB |     89%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla M60           Off  | 000057C7:00:00.0 Off |                  Off |
| N/A   40C    P8    14W / 150W |     11MiB /  8129MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     15257      C   ./darknet                                   1439MiB |
+-----------------------------------------------------------------------------+

Is multi-GPU support just for training?

Getting this error despite having CUDA 8 installed w/ CUDNN and OpenCV 3.2.0:

gcc -Iinclude/ -Isrc/ -DOPENCV pkg-config --cflags opencv -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -c ./src/gemm.c -o obj/gemm.o In file included from ./src/utils.h:5:0, from ./src/gemm.c:2: include/darknet.h:14:30: fatal error: cuda_runtime.h: No such file or directory compilation terminated. Makefile:87: recipe for target 'obj/gemm.o' failed make: *** [obj/gemm.o] Error 1

Where is that file supposed to be? Thanks!

    are you on Linux? maybe permissions issue?

Originally posted by @robisen1 in https://github.com/pjreddie/darknet/issues/2087#issuecomment-606192206

Loaded: 0.000050 seconds Region 82 Avg IOU: nan, Class: nan, Obj: nan, No Obj: nan, .5R: 0.000000, .75R: 0.000000, count: 2 Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 Region 106 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 50198: -nan, -nan avg, 0.000010 rate, 0.144766 seconds, 50198 images Loaded: 0.000050 seconds Region 82 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 Region 94 Avg IOU: nan, Class: nan, Obj: nan, No Obj: nan, .5R: 0.000000, .75R: 0.000000, count: 1 Region 106 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 50199: -nan, -nan avg, 0.000010 rate, 0.143455 seconds, 50199 images Loaded: 0.000073 seconds Region 82 Avg IOU: nan, Class: nan, Obj: nan, No Obj: nan, .5R: 0.000000, .75R: 0.000000, count: 1 Region 94 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 Region 106 Avg IOU: -nan, Class: -nan, Obj: -nan, No Obj: nan, .5R: -nan, .75R: -nan, count: 0 50200: -nan, -nan avg, 0.000010 rate, 0.141549 seconds, 50200 images Saving weights to backup/yolov3-voc.backup Saving weights to backup/yolov3-voc_final.weights

Designing Network Design Strategies Through Gradient Path Analysis: https://arxiv.org/abs/2211.04800

ELAN network is +1.9% AP more accurate and faster than YOLOR Object Detector.

While YOLOR was the best on COCO dataset and Waymo self-driving dataset in speed/accuracy even 1 year after release: https://waymo.com/open/challenges/2021/real-time-2d-prediction/

YOLOR: https://arxiv.org/abs/2105.04206 YOLOR on Waymo: https://arxiv.org/abs/2106.08713

Hello, Joseph Redmon. I recently trained v1, v2 and v3 separately on the voc2007+2012 training dataset using darknet. Later, map was detected on the 2007 testing dataset, but the result was beyond my expectation: on the premise of using the pre-training model provided by the website(https://pjreddie.com/), v1 reached nearly 85% map, but the mAP of v2 and v3 were 74% and 69% respectively, both of which were lower than v1. This made me very confused and curious. For v1, I use: ./darknet yolo train cfg/yolov1/yolo.train.cfg extraction.conv.weights For v2, I use: ./darknet detector train cfg/voc.data cfg/yolov2-voc.cfg darknet19_448.conv.23 For v3, I use: ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 For mAP test, I use: ./darknet detector map cfg/voc.data cfg/yolov1.cfg yolo_v1_final.weights ./darknet detector map cfg/voc.data cfg/yolov2-voc.cfg yolo_v2_final.weights ./darknet detector map cfg/voc.data cfg/yolov3-voc.cfg yolo_v3_final.weights Can you give me some suggestions? Regards

Hello, Joseph Redmon.

I'd love to hear from the original developer how to draw the correct bounding box for OCR object detection. I'm arguing with someone who says the correct way to recognize an object is to draw a perfectly tight bounding box that does not differ even by 1px from the object's outline. In my common sense, to solve problems such as overfitting, it is necessary to hit the Bounding Box with some margin around the object to some extent. We would like to hear the correct answer to this debate from you, the original AI engine developer.

Attachments are an example, and the argument is that you should never set the margin of the bounding box as wide as the 8 or Waterlevel Scale in the picture. Is this the wrong way to lower the recognition rate for 8 or Waterlevel Scale?

We would like to hear the correct answer to this debate from you, the original YOLO Darknet AI engine developer.

Please give me some answers.

Regards

I have trained a custom YoloV4 model. However, upon inference, I'm getting this error on my server:

./darknet: error while loading shared libraries: libopencv_core.so.2.4: cannot open shared object file: No such file or directory

I tried re-installing OpenCV and libopencv-dev, but the error persists.

The inferencing code and the model run FINE on Colab, which is running OpenCV 4.6.0. Some other threads that mentioned this error suggested changing OpenCV to 2.4, however, this will not be possible on the organization's server. However, in the conda environment I'm using, I have tried multiple OpenCV versions but the error still shows up. Further, it runs fine on colab, so the issue can't be with OpenCV version.

Kindly suggest how I can resolve this, Thanks.