I currently have a small pet project where I think some simple ML would be cool but I don't know where to start so these things are great.
Basically my use case is that I have a bunch of 64x64 images (16 colors) which I manually label as "good", "neutral" or "bad". I want to input this dataset and train the network to categorize new 64x64 images of the same type.
But it's still too hard to understand exactly how I can create my own dataset and how to set it up efficiently (the example is using 32x32 but I also want to factor in that it's only 16 colors; will that give it some performance advantages?).
I currently have a small pet project where I think some simple ML would be cool but I don't know where to start so these things are great.
Basically my use case is that I have a bunch of 64x64 images (16 colors) which I manually label as "good", "neutral" or "bad". I want to input this dataset and train the network to categorize new 64x64 images of the same type.
The closest I've found is this: https://gist.github.com/sono-bfio/89a91da65a12175fb1169240cd...
But it's still too hard to understand exactly how I can create my own dataset and how to set it up efficiently (the example is using 32x32 but I also want to factor in that it's only 16 colors; will that give it some performance advantages?).