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1import tensorflow as tf
2import pickle
3import numpy as np
4
5# Load the model
6model = tf.keras.models.load_model('model.h5')
7
8# Load the processor
9with open('processor.pkl', 'rb') as f:
10 processor = pickle.load(f)
11
12# Example inference
13# your_landmark_data should be preprocessed using the same processor
14predictions = model.predict(your_landmark_data)
15predicted_classes = np.argmax(predictions, axis=1)model.h5: Complete Keras model (recommended for inference)model_weights.h5: Model weights onlyprocessor.pkl: Data processor for landmark preprocessingconfig.json: Model configuration and metadatatraining_history.json: Training metrics and historyinference_example.py: Example inference scriptrequirements.txt: Required dependenciesrequirements.txt for the complete list of dependencies.