In such a case we then need to write something that will map the files with actual classes for us in a csv file for us to refer after the predictions were made?
Or lets play smart and move all your test images to validation set and those existing in validation to train and now update the test folder has a single image of different classes?
Or we can use the model to predict on single images…?
I think this could be a solution but not the ideal one.
I think there is not a requirement of a minimum of images in test set and i could test with one for each class, i think but that would not be a solution neither.
i have been checking ImageClassifierData class and i dont see it is possible to do what i need and it’s quite odd because this is possible in keras.
It’s possible here also but we need to hard-code such stuff(not sure whether it’s already there)
It should be like checking all the files in a particular directory of a particular class and checking with what class our model has predicted that images to be in…(using os, glob etc)
Following this discussion with intent, because I too want to have funky images in the test folder and see how the model performs and realized that the current workflow does not probe the test dataset.
Found these threads discussing similar:
Looks like these solve part of the problem:
Here’s my status:
I am able to pass in the test data folder as
Thanks! this helped me a lot. I used your code as i were sending a submission and at least it maps the name of the file with the label assigned. With this, now i can check if testing is doing fine, i just have to code some more things to make it more ‘automatic’
ups sorry, i know it is not your code, i just typed fast…
What i did was basically the same as @SlowLlama did, with the obvious changes to fit my dataset characteristics and in advance i apologize if my code is very basic, i just started with python.