Improving Image Classification Model for Brand Recognition - Deep Learning

Hello everyone,

I hope you’re all doing well.

I’m posting this message to seek assistance in improving my image classification model. I’ve adapted the workflow for classifying dogs and cats to my specific use case.

I’d like to use images of storefronts or advertisements to recognize certain brands. This involves deciphering logos or brand names on storefronts and signs.

Unfortunately, the expected results are not satisfactory. I’ve tried changing several parameters, such as the image size. Initially, in the dogs and cats workflow, the images are 150150 pixels. I’ve tried increasing the size up to 700750 pixels, as beyond that, the Keras Learning node crashes. I’ve also tried adjusting the number of epochs, up to 7, as beyond that, it also crashes.

Additionally, my dataset is not very large, consisting of only 350 images with an 80/20 split. Unfortunately, it’s challenging to increase the size of my dataset.

So, do you have any solutions to improve my model ? Perhaps regarding the appropriate function or parameters that effectively recognize characters? Or maybe preventing the Learned node from crashing ?

Or improving the quality, here’s a preview after transformation. Note that the original photo is sharper.

What the image look in the image reader node.

After rezising to 700*750

The original

All suggestions are welcome. Thank you very much.