Yes, definitely agree. The project started as one thing, then turned into another, then another. I was only doing the coin grading for fun and wasn't planning on actually implementing it anywhere. So I switched gears and just focused on building a high resolution ResNet, regardless of what would be best for the actual coin grading.

There are probably better solutions, especially for this size of a dataset, and maybe a sliding window is necessary to achieve very high accuracy.

But I think this model can still be useful and preferable for some datasets of large images with fine patterns. Or at the very least preferred for simplicitys sake.

Excel lol

I didn't try it. I decided to just bust this out and move on to the next project. It was fun though.

A 224x224 image is sufficient for most classification tasks, but there are instances where the fine details of a large image need to be analyzed. Hi-ResNet is the ResNet50 architecture expanded (with the same rules from the paper) to allow for higher resolution images.

I was working on a coin grading project and found that accuracy could not surpass 30% because the image size completely obscured the necessary details of the coin. One option is to tile the image, run them each through a classifier, and combine the outputs. Another is to just try a classifier with a higher resolution input, which is actually kind of difficult to find. Maybe I did not look hard enough, but I figured it would be a good exercise to build this out regardless.

It may come in handy for you later. It's a very simple function with 3 arguments that returns a Hi-ResNet Tensorflow model.