Assess the model against a recognized benchmark dataset is the correct option because it gives an objective, fair, and repeatable measure of accuracy that was not used during training.

Evaluating on a standardized held out benchmark ensures results can be compared across models and research efforts and it reduces the risk of data leakage. A benchmark that covers or closely matches the 180 categories gives a reliable view of generalization and supports statistical testing and detailed error analysis.

Amazon CloudWatch is focused on operational telemetry and infrastructure metrics and it does not provide mechanisms for held out dataset evaluation or computing model classification accuracy.

Deploy the model to production and rely on user feedback postpones proper validation until after release and it risks harming users while producing noisy and biased signals that are not a reproducible accuracy measurement.

Test accuracy using a small slice of the training set creates optimistic estimates because of data leakage and overfitting and it does not reflect how well the model will perform on new images.

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