How to preprocess data for image classification from a .txt file? Basically my issue is that im building an image classification model using AlexNet. I have this pre-split dataset thats already split into training, test, validation. However the issue is that these splits are in .txt files (e.g. trainingsplit.txt) and inside the .txt file is a list of image_001.png files etc. How do I extract this data and pass it through my preprocessor? This is what I would normally do for preprocessing:

train_dir = '/content/gdrive/My Drive/alexNet/Training'
validation_dir = '/content/gdrive/My Drive/alexNet/Validation'

train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=40,
      width_shift_range=0.2,
      height_shift_range=0.2,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest',
      )

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        train_dir,  # This is the source directory for training images
        target_size=(224, 224),  
        batch_size=20,
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(224, 224),
        batch_size=20,
        class_mode='binary')


Edit: Content of training.txt

image_001.jpg
image_002.jpg
image_014.jpg
image_017.jpg
etc.


The training data is a .txt file containing several rows of text, which are the names of different images. This txt file is inside the main directory Dataset, which contains class directories like Apples, Bananas, Oranges, etc.
I am assuming from the description inside main directory there are more directories of  Apples, Bananas, Oranges, etc. and inside them you have .txt files containing information about the images.

import shutil

with open(file_path, 'r') as f:
    img_names = f.readlines()
    img_names = [img.strip() for img in img_names]

for i in range(len(img_names)): shutil.move(img_names[i], folder_path)


Once images are moved into there respective folder you can use ImageDataGenerator approach to train model.