Which Image resolution should I use for training for deep neural network?
CIFAR dataset is 32px*32px,
MIT 128px*128px,
Stanford 96px*96px.
Following the advice here https://towardsdatascience.com/boost-your-cnn-image-classifier-performance-with-progressive-resizing-in-keras-a7d96da06e20
“small-image models are much faster to train.”
“Here is a smoothed kernel-density plot of image sizes in our “Open Fruits” dataset:”
We see here that the images peak at around 128x128 in size. So for our initial input size we will choose 1/3 of that: 48x48.
Now it’s time to experiment! What kind of model you end up building in this phase of the project is entirely up to you.” (https://towardsdatascience.com/boost-your-cnn-image-classifier-performance-with-progressive-resizing-in-keras-a7d96da06e20)
I’ll have a look at the chicken images, and see how to scale them down. Maybe ffmpeg or convert or imagemagick pre-processing is better. But we’ll get there soon enough.