# Load your custom dataset data = ImageClassifierDataLoader.from_folder(flower_path) train_data, test_data = data.split(0.9) # Customize the pre-trained TensorFlow model model = image_classifier.create(train_data, model_spec=efficienetnet_lite0_spec) # Evaluate the model loss, accuracy = model.evaluate(test_data) # Export as TensorFlow Lite model. model.export('image_classifier.tflite', 'image_labels.txt')
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