So basically, I am training humans and seperate them from animals(cats)
I have training data full of humans,cats and val data humans,cats. I use imagefolder btw.
However, I wanted to try sth new:
In train folder, I put “human” folder that has human pictures.
In val folder, I also put “human” folder that has cat pictures instead. So I expected that to have 0% accuracy, because of overfitting and not accurate image clusters.
If I had human folder with human pics in train AND human,cat in val the accuracy is 50% which is normal.
The rest of the classifications is fine as well. However, that thing really gave me weird feelings on my model.
Thanks!