Logo_classifier_train.py

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV3Small
from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, Conv2D, MaxPooling2D, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

IMG_SIZE = 224
BATCH_SIZE = 16
EPOCHS = 10
FINE_TUNE_EPOCHS = 5
DATA_DIR = 'Logos'

train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    validation_split=0.2,
    rotation_range=20,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

train_generator = train_datagen.flow_from_directory(
    DATA_DIR,
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='binary',
    subset='training',
    shuffle=True
)

val_generator = train_datagen.flow_from_directory(
    DATA_DIR,
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='binary',
    subset='validation',
    shuffle=False
)

base_model = MobileNetV3Small(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False, weights='imagenet')
base_model.trainable = False

x = base_model.output
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.3)(x)

x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.3)(x)

x = GlobalAveragePooling2D()(x)                  
x = Dense(128, activation='relu')(x)
x = Dropout(0.4)(x)

authenticity_predictions = Dense(1, activation='sigmoid')(x)

model = Model(inputs=base_model.input, outputs=authenticity_predictions)

early_stop = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True, verbose=1)
checkpoint = ModelCheckpoint('authenticity_classifier_mobilenet.keras', monitor='val_accuracy', save_best_only=True, verbose=1)

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
print("\n=== Training Phase 1: Frozen Base Model ===")
model.fit(
    train_generator,
    validation_data=val_generator,
    epochs=EPOCHS,
    callbacks=[early_stop, checkpoint],
    verbose=1
)

base_model.trainable = True
for layer in base_model.layers[:-30]:
    layer.trainable = False

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(
    train_generator,
    validation_data=val_generator,
    epochs=FINE_TUNE_EPOCHS,
    callbacks=[early_stop, checkpoint],
    verbose=1
)