{"knowledge_schema": {"broad_category": "Computer Science \u2192 Machine Learning \u2192 Image Classification", "refinement": "This problem focuses on improving the accuracy of an image classification model through data augmentation techniques.", "specific_scope": "The task involves selecting the most effective image augmentations for a model designed to classify images of mammals captured by a camera trapping system.", "goal": "Identify the image augmentation techniques that will enhance the model's ability to accurately classify images of various mammal species."}, "summary": "This problem revolves around enhancing the accuracy of an image classification model for detecting specific mammal species in camera trap images. The focus is on selecting appropriate image augmentations that can improve the model's performance during deployment. Given the nature of the images captured under infrared light and the potential variability in animal appearances, augmentations that simulate real-world conditions, such as motion blur, are likely to be most beneficial. The goal is to determine which augmentation techniques will best prepare the model for accurate classification in diverse scenarios."}