Exploratory Data Analysis with Image Datset In Machine Learning Kernels on Kaggle I often see EDAs with structured data. So, I was wondering, if there are any recommended/standard procedures for EDA with image datasets. What kind of statistical analyses do you conduct, what kind of plots do you draw and what aims do you have in mind?
Have a look at The HASYv2 dataset. I tried to do as much of the exploratory work as possible to make sure that others can directly try more interesting thing with the dataset.

Image format specific stuff


(Minimum / Median / Mean / Maximum) (width / height / area)
Image formats
Timestamps
Exiff meta data


For this kind of stuff, you might want to have a look at edapy.

Image/ML specific stuff

Things you can do with images:


Compute the mean image


Mean image by class

Eigenfaces (or rather "Eigenimages")
Fisher-Faces


You can compute the correlation of pixels, e.g. Figure 3:



Classification-specific stuff


Plot the distribution of classes.
Behaviour of standard classification algorithms (CNNs, VGG-16)
Confusion Matrix Ordering (page 48 - 52, especially Figure 5.12 and 5.13): Find similar classes