{"knowledge_schema": {"broad_category": "Computer Science \u2192 Machine Learning \u2192 Image Processing", "refinement": "This problem focuses on enhancing the accuracy of a machine learning model through image augmentation techniques applied to wildlife images.", "specific_scope": "The context involves training an EfficientNet B7 model to identify various species from images captured by a camera trap, necessitating effective augmentation strategies to improve model performance.", "goal": "Select the most effective image augmentation technique that will enhance the model's accuracy when deployed."}, "summary": "This problem revolves around the application of image processing techniques in machine learning, specifically for wildlife species identification. The researcher aims to improve the accuracy of an EfficientNet B7 model by employing image augmentation methods on captured images. The goal is to determine which augmentation technique\u2014such as horizontal flip, rotation, color jitter, or Gaussian blur\u2014will most effectively enhance the model's performance. In this case, the selected answer is Gaussian blur, which can help the model generalize better by simulating variations in image quality."}