Training YOLO V2 with multiple (more than one) classes

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Zahra Moayed 2019-8-5

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评论： Anushikha Singh 2022-12-13

Hi all,

When I train YOLOV2 with single class (person) using trainYOLOv2ObjectDetector, I can get precision/recall of 0.92 but when I add another class (car) with same images and just few car labels, the accuracy is 0, meaning even the person cannot be detected in any of the images even my training images!

I even use AnchorBox estimation and treid many times.

All the matlab examples are tarined only on single objects but how about if we have more than one class to be trained? Does anyone have any success to help me please?

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jingxue chen 2020-7-15

I meet the same problem now！Have you find some solutions？

Sanjeev Madhave 2020-11-18

Hi Zahra Moayed,

I was also trying to train the yolo with multi class. I have a doubt. How did you mapped the training dataset table?

In my case, in some training image one class may not be there. In such cases how to fill the table? just leave it as empty? in that case matlab is throwing error. any help is much appreciated

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回答（3 个）

Srivardhan Gadila 2019-8-14

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编辑：Srivardhan Gadila 2019-8-14

The procedure is same for both single and multi-class. The zero accuracy may imply that the dataset is biased, so try having nearly equal number of labels for cars and persons.

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Srivardhan Gadila 2019-8-19

In general the first case should produce the good results i.e., having the following equally: Images/Frames having 1. Only vehicles 2. Only Pedestrains 3. Both Vehicles and Pedestrains.

Zahra Moayed 2019-8-26

Hi Srivardhan,

To update, I built another labelling session to contain both People and Car. Number of objects are 137 and 141 for people and car, respectively so the dataset is totally fine.

Still after multiple trial, I got 0 accuracy, nothing is detected at all.

One question: do you train any network with YOLOV2 with multiple classes before? I want to narrow down the problem to see if the issue is from my side or the YOLO V2 release has got issue.

Thanks a lot.

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Zahra Moayed 2019-10-7

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Hi,

Is there any updates in the new version of Matlab regarding this issue? Or does anyone have any experience in training multiple classes?

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Damjan Konjevod 2022-4-1

Any updates? I have the same problem, can detect 1 class but in case of multiple classes 0 recall

Anushikha Singh 2022-12-13

any updates?

m still facing same issue

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Sunny Guha 2022-4-6

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Hi Zahra

Please refer to this R2022a example on training YOLO v2 for multiple classes:

https://www.mathworks.com/help/vision/ug/multiclass-object-detection-using-yolo-v2-deep-learning.html

In general, there could be multiple issues that hinder performance of networks on multiple class datasets. Here are few of the things you can try to resolve the issues:

Ensure the classes are close to balanced. If you cannot obtain more labels resort to data augmentation. The example I linked above contains steps on how to perform augmentation.
Change backbone/feature extraction layer. Object detectors have a hard time detecting smaller objects. Bigger (spatial resolution) feature extraction layers can detect smaller objects.
Try a different detector like yolov4 which perform multiscale detection.

Hope this helps.