Yeah the csv file has three columns separated into emotion, pixels, and usage. Emotion corresponds to the labels whereas usage corresponds to training/test/val, and the pixels column is made up of all of the pixel values used to make the image. It seems to produce much quicker training times than using the images, which is my main reason for wanting to use it. Training on .csv takes around 10 seconds per epoch whereas images take 10 minutes or so.

They both produce the same result, a trained model which can make predictions on facial expressions, however its felt weird throughout the entire process that the model trains so quick, you know? I've been led to believe that machine learning is an extremely time intensive process but for me it hasn't took long at all, so I was wondering if there's some fundamental error with using the .csv data instead of the images. Hopefully it should be fine though, I don't see the issue myself if it produces the same result.

Like I said the .csv data. Its the same data as the image dataset with one of thr columns containing the pixel values of the images, meaning it can reconstruct the image from the file.

For my final year uni project I need to train a TensorFlow CNN on the FER-2013 dataset. When training the model on data from the .csv file instead of locally stored images the model trains significantly faster, with around 10 seconds per epoch as opposed to 10 minutes or so for the images. My question is it okay for me to use .csv data instead of locally stored images for this image classification task? I know I won't be able to apply data augmentation as easily but I can't think of any other downsides which would disqualify me from using the .csv data instead of the images