Multiple overlapping boxes even with very low NMS threshold

[anant15]

I am getting these multiple overlapping boxes in the prediction after training YOLOv4 on my custom dataset. I use confidence score = 0.8, and NMS threshold = 0.5. I tried reducing the NMS threshold to 0.05, but the number of boxes somewhat reduced but the boxes aren't well fitted. Please suggest ways to improve training or mitigate this problem in general.

PS: I am using 9 pairs of custom anchors, calculated for my dataset.

[AlexeyAB]

It seems you disabled NMS. What command do you use? Attach cfg-file.

I tried reducing the NMS threshold to 0.05, but the number of boxes somewhat reduced but the boxes aren't well fitted.

Show image.

[anant15]

Thanks @AlexeyAB for a super quick response. Really grateful to you. I have already attached the image in my first comment, The image is a bit heavy and takes a little time to load.

For training, I used a command like this

./darknet detector train ../cfgYolov3/yolov4-custom.data ../cfgYolov3/yolov4-custom.cfg ../backupYolov3/july24_4_yolov4/yolov4-custom_best.weights -dont_show -mjpeg_port 8090 -map 2>&1 | tee -a ../log_july24_4_yolov4.txt

For checking predictions, I am using my own custom Python script as I wanted to check the results visually on multiple images.

The cfg file is as follows. I intentionally used leaky instead of mish because it was not supported with the current OpenCV version that I had; didn't to re-install and break things.

yolov4.txt

[AlexeyAB]

Show detection result by using command ./darknet detector test ... with the same image

OpenCV 4.4.0 supports YOLOv4 and mish: https://opencv.org/releases/

[anant15]

./darknet detector test ../cfgYolov3/yolov4-custom.data ../cfgYolov3/yolov4-custom.cfg ../backupYolov3/july24_4_yolov4/yolov4-custom_last.weights -ext_output ../images_4/E000V1920-20200530062056_1fps_hand_head_annotations_000252.jpg

On using this, I get this error

Upgrading OpenCV will be a little challening. I work on an organization's computer, and I need to take permissions before installation or compiling anything. The training went well with leaky too.

[AlexeyAB]

Show prediction.png file

[anant15]

predictions.jpg is a garbage 10X10 file which looks like a black blob.

Also, it showed Cannot load image ../images_4/E000V1920-20200530062056_1fps_hand_head_annotations_000252.jpg in the testing log.

[AlexeyAB]

use correct path

[anant15]

This came out pretty well, I think there is some problem with NMS. I am using indexes = cv2.dnn.NMSBoxes(boxes, confidences, confThresh, nmsThresh), but the interesting thing is that this problem didn't arise until I was using the YOLOv3 model. But I will look into it. Thanks.

Just a request, if you could please add a functionality to check predictions on images placed in a directory, and save corresponding outputs somehwhere, it will be really helpful. Thanks a lot.

[AlexeyAB]

So the issue is in your Python code.

Read: https://github.com/opencv/opencv/wiki/ChangeLog#version440

State-of-art Yolo v4 Detector: #17148. Disabled per-layer NMS for [yolo] layers (Yolo v3 and Yolo v4) as incorrect - use cv::dnn::NMSBoxes over all the detections.

[anant15]

Thanks a lot, @AlexeyAB , isn't somehow the support for testing the predictions for multiple images be done? I am not a C/C++ user and finding it difficult to extend your code.