You might apply image classification to a different task (e.g., places, people, foods, etc.). Since you should be able to get a baseline approach working quickly, here are some ways you can deepen this kind of project:
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Analyze the model’s errors, both quantitatively and qualitatively.
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Compare several approaches. You can consider differences in model architecture, specific task, hyperparameter choices, inclusion/exclusion criteria, etc. Remember to think about the choice of metrics and the uncertainty involved in any estimate of them.
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Generate explanations of the classification decisions, using the model interpretation methods described in the book or otherwise.
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Discuss how you were able to tune the performance of the classifier.