Case context: You are building a computer vision model to recognize cats in images, a task that humans do well. Your team has set up an Eyeball dev set but is unsure how to evaluate if its size is sufficient. The team currently has only a few misclassified examples to review, which does not show distinct error trends.

Question: Based on the goal of using an Eyeball dev set, what should your team diagnose or decide regarding the size of this set to ensure it meets its intended purpose?

Sample answer: The team should decide to increase the size of the Eyeball dev set. The primary objective is to ensure the Eyeball dev set is large enough to give a representative sense of the algorithm's major error categories. Since the current set is too small to show distinct error trends, expanding it will allow for manual inspection of a sufficient number of errors, which is crucial for tasks humans do well like cat recognition.

Key points:

• Diagnose the current Eyeball dev set size as insufficient to show major error categories.
• Decide to increase the size of the Eyeball dev set.
• Connect the sizing requirement to the goal of identifying major error categories in human-level tasks like cat recognition.

Rubric: The response must diagnose the current Eyeball dev set as too small and decide to expand it. It must explain that the expansion is necessary to identify and categorize the major error categories of the algorithm, particularly for a human-level task like cat recognition.