borb VS cleanX

Have you looked at borb? I’m not sure if it’s exactly what you need I found it useful when doing something similar to you

You might take a look at borb

I will also perform a quick comparison with the borb library.

Get borb from source on GitHub, or download using PyPi.

Unfortunately, it's not minimal, and is quite complicated, but it's the one I wrote, so I can answer your questions if you have any: https://github.com/drcandacemakedamoore/cleanX/tree/main/conda-pkg and https://github.com/drcandacemakedamoore/cleanX/blob/main/setup.py (the links are to Conda-specific setup). I also created Conda builds for few other projects (not written by me):

If you work with medical images you may be interested to look at https://github.com/drcandacemakedamoore/cleanX . It does data cleaning for chest X-rays (DICOM or JPEGs) and associated information.

A few months ago I posted to the machine learning channel about a package I thought some might find helpful for radiology images. The package, cleanX, has since evolved, and I think it can be even more helpful, and to actual data science practitioners in general. While it began for chest X-rays, many sections are generalizable for tabular data linked to images. Enjoy!

I faced a similar issue on chest X-rays. You can see what I did already about it in the library cleanX, but to summarize for an initial idea I averaged all the images in each set, then subtracted. It won't tell you much, but it will point to areas where things are different- in the case of roads with an without tractors, it will show big differences on the roads (unless there is a difference in what surrounds the roads)

There is no machine learning. The algorithms are meant to clean up the data before you build any machine learning algorithms. There are several ways to predict non-Xray images. One is to compare them to a small average image of all the X-rays. Take a look at the code (https://github.com/drcandacemakedamoore/cleanX), and you will find a couple of others...

An excellent suggestion. I have thought about how best to implement demonstrations, because when I ran my code over a few Kaggle datasets, I found problems in them as I show here, but to know if I caught ALL of them or even most of them would require examining every single image by hand. But maybe the answer is to show a machine learning training task with dirty and cleanX cleaned data to show it makes a difference. I'll try to get to it soon.