maskmodel.py

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define the path to your dataset
dataset_path = "E:\Face Mask Dataset"

# Use ImageDataGenerator for data augmentation
datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

# Load and preprocess training data
train_generator = datagen.flow_from_directory(
    dataset_path,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary',  # or 'categorical' if you have more than two classes
    subset='training'
)

# Load and preprocess validation data
validation_generator = datagen.flow_from_directory(
    dataset_path,
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary',
    subset='validation'
)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(2, 2))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))

model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(2, 2))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))  # Use 'softmax' if more than two classes

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit(
    train_generator,
    epochs=10,  # Adjust the number of epochs based on your dataset and resources
    validation_data=validation_generator
)

model.save('face_mask_detection_model.h5')