CNN training accuracy flatlines

I'm training a CNN from scratch to do tagging of images. And my training is going nowhere. I was hoping someone could help me identify an obvious error. I would like to end up with a network that given an image can output 0 or 1 if it contains a face or not. I know there are a lot of pre-trained models that can do this, but this is only a starting point for me. My dataset consists of a CSV that looks like this. I have chose this format, because I plan to add more tags to the dataset in the future. filename,face img1.jpg,0 img2.jpg,1 ... ... which I load into a pandas dataframe with load_csv(). Then I create a training generator. datagen = ImageDataGenerator(validation_split=0.2) train_generator = datagen.flow_from_dataframe( dataframe=df, directory=image_directory, x_col='filename', y_col=label_strings, subset='training', # Specify subset as training class_mode='raw', target_size=(270, 480), batch_size=32, shuffle=True ) ... and I also create a validating generator, using similar settings. Here's my network architecture: model = Sequential([ layers.InputLayer(input_shape=(270, 480, 3)), # # data augmentation, layers.RandomFlip("horizontal_and_vertical"), layers.RandomRotation(0.025), # preprocessing layers.Rescaling(1./255), # do the work layers.Conv2D(16, 3, padding='same'), layers.Activation("relu"), layers.MaxPooling2D(), layers.Conv2D(32, 3, padding='same'), layers.Activation("relu"), layers.MaxPooling2D(), layers.Conv2D(64, 3, padding='same'), layers.Activation("relu"),…