Convert folder of images with labels in CSV file into a tensorflow Dataset

It is possible but making it correct the first time will be managed it easy when application process or adjusting to the current process is because correcting logic later is sometimes a massive task.

Adjust the label and target directory variable fields targeted to your observation.

[ Sample ]:

import tensorflow as tf
import tensorflow_io as tfio

import pandas as pd

import matplotlib.pyplot as plt

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
list_label_actual = [ 'Candidt Kibt', 'Pikaploy' ]

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: Dataset
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variables = pd.read_excel('F:\temp\Python\excel\Book 7.xlsx', index_col=None, header=[0])

list_label = [ ]
list_Image = [ ]
list_file_actual = [ ]

for Index, Image, Label in variables.values:
    print( Label )
    list_label.append( Label )
    
    image = tf.io.read_file( Image )
    image = tfio.experimental.image.decode_tiff(image, index=0)
    list_file_actual.append(image)
    image = tf.image.resize(image, [32,32], method='nearest')
    list_Image.append(image)


list_label = tf.cast( list_label, dtype=tf.int32 )
list_label = tf.constant( list_label, shape=( 33, 1, 1 ) )
list_Image = tf.cast( list_Image, dtype=tf.int32 )
list_Image = tf.constant( list_Image, shape=( 33, 1, 32, 32, 4 ) )

dataset = tf.data.Dataset.from_tensor_slices(( list_Image, list_label ))
list_Image = tf.constant( list_Image, shape=( 33, 32, 32, 4) ).numpy()

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: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
    tf.keras.layers.Normalization(mean=3., variance=2.),
    tf.keras.layers.Normalization(mean=4., variance=6.),
    # tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    # tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Dense(256, activation='relu'),
    # tf.keras.layers.Reshape((256, 225)),
    tf.keras.layers.Reshape((256, 32 * 32)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(196, return_sequences=True, return_state=False)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(196)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(192, activation='relu'),
    tf.keras.layers.Dense(2),
])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class custom_callback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if( logs['accuracy'] >= 0.97 ):
            self.model.stop_training = True
    
custom_callback = custom_callback()

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: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.000001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=tf.keras.losses.Reduction.AUTO,
    name='sparse_categorical_crossentropy'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'] )

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: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50, callbacks=[custom_callback] )

plt.figure(figsize=(6,6))
plt.title("Actors recognitions")
for i in range(len(list_Image)):
    img = tf.keras.preprocessing.image.array_to_img(
        list_Image[i],
        data_format=None,
        scale=True
    )
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])
    plt.subplot(6, 6, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(list_file_actual[i])
    plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" +  str(list_label_actual[tf.math.argmax(score)]))
    
plt.show()

input('...')

[ Output ]:
Sample