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I built a digital scale reader using Darknet's YOLOv4Tiny. It is having trouble confusing 2's and 5's which leads me to believe that I am doing some unwanted data augmentation during training. (The results are mostly correct, and glare could be a factor, but I am expecting better results).

I have referenced this post: Understanding darknet's yolo.cfg config files

and the darknet github: https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-%5Bnet%5D-section

Below is a link to the yolov4-tiny.cfg that I modified for my model: https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov4-tiny.cfg

And a snippet from the link above:

[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

Am I correct that angle=0 means that there is no rotation?

Are there any other possible ways I might be augmenting my data that could cause an issue?

Edit: If I wanted, how could I eliminate all data augmentation?

Or do I just need more data (currently 2484 images for 10 digit classes)?

greenzebra
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2 Answers2

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horizontal flip is applied by default, add "flip=0" to disable. https://github.com/AlexeyAB/darknet

Song Yuan You
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Here, the angle, saturation, exposure, and hue all are part of data augmentation. You can eliminate all data augmentation by setting the value to 0. As data argumentation's values are hyperparameters, modifying the value of these could lead the accuracy to better or worse both. Here I suggest you keep the value given by darknet the same as they found these values are good to get good accuracy. And by all these 4 types of data augmentation darknet initially generated all images. If you add more images without replication it is always good to add more images for the deep learning model to learn the necessary complexity during training.

Abdullah Al Muksit TAMIM
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