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diff --git a/image-attribute.py b/image-attribute.py
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--- /dev/null
+++ b/image-attribute.py
@@ -0,0 +1,75 @@
+import tensorflow as tf
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Set the paths to your DeepFashion dataset
+train_data_dir = 'data/train'
+validation_data_dir = 'data/validation'
+
+#
+# Set the number of classes and batch size
+num_classes = 50
+batch_size = 32
+#
+# Data augmentation and normalization
+train_datagen = ImageDataGenerator(
+    rescale=1.0 / 255,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True
+)
+
+validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
+#
+# Load the ResNet50 model without the top layer
+base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+
+# Add a global average pooling layer
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+
+# Add a fully connected layer with 1024 units
+x = Dense(1024, activation='relu')(x)
+
+# Add the final output layer with the number of classes
+predictions = Dense(num_classes, activation='sigmoid')(x)
+
+# Create the model
+model = Model(inputs=base_model.input, outputs=predictions)
+
+# Freeze the base model layers
+for layer in base_model.layers:
+    layer.trainable = False
+
+# Compile the model
+model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])
+
+# Load and preprocess the training and validation data
+train_generator = train_datagen.flow_from_directory(
+    train_data_dir,
+    target_size=(224, 224),
+    batch_size=batch_size,
+    class_mode='categorical'
+)
+
+validation_generator = validation_datagen.flow_from_directory(
+    validation_data_dir,
+    target_size=(224, 224),
+    batch_size=batch_size,
+    class_mode='categorical'
+)
+
+# Train the model
+model.fit(
+    train_generator,
+    steps_per_epoch=train_generator.samples,
+    epochs=10,
+    validation_data=validation_generator,
+    validation_steps=validation_generator.samples
+)
+
+# Save the trained model
+model.save('deepfashion_attribute_model.h5')