Case context: A team's Siamese cat detector pipeline outputs 0 (no Siamese cat) for an image that actually contains a Siamese cat. When the team inspects the pipeline's intermediate output, they find that the cat detector correctly detected and cropped the cat, producing a clear, accurate crop of the animal.

Question: Given that the crop is accurate, which pipeline component is most likely responsible for this mistake, and what should the team examine next?

Sample answer: Since the cat detector produced an accurate crop, the cat detector is not responsible for this particular error. The mistake must have occurred in the cat breed classifier, which received the correct cropped image but failed to classify it as Siamese. The team should examine the classifier's prediction on that specific crop to confirm it misclassified the breed, and then look for patterns across similar misclassified crops (e.g., lighting, pose, or breed similarity issues) to guide improvements to the breed classifier rather than the detector.

Key points:

• An accurate crop rules out the cat detector as the source of this error
• The cat breed classifier is the likely source since it received a correct crop but still output the wrong result
• Error analysis by parts uses the accuracy of each stage's output to attribute blame
• Next step is inspecting the classifier's behavior on the specific and similar crops
• Goal is directing improvement effort to the actual faulty component

Rubric: Full credit correctly attributes the error to the cat breed classifier because the crop was accurate, and identifies that the team should inspect the classifier's output on that crop and look for patterns to target improvement.