{'fraction_non_alphanumeric': 0.011655011655011656, 'fraction_numerical': 0.0, 'mean_word_length': 5.362962962962963, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A study on the application of knowledge distillation in ship type classification model development

In order to identify an object of interest in a given image with high accuracy, it is essential to select cumbersome deep learning models. However, these models are computationally expansive and cannot be deployed when limited environments. We experiment on a ship type classification model that supports fast inference while inheriting the performance of the teacher network through knowledge distillation and share the results. As can be seen from the experimental results, through knowledge distillation, we got a clue that we can learn a deep learning network that can provide real services. Through additional data learning and hyperparameter tuning, it can be deployed in the actual ship type classification system and utilized in various applications.