Where's Waldo serves as an intriguing dataset for testing a visual recognition API through Custom Training, as demonstrated during a Hack Day project at Clarifai. The project involved using a pre-existing dataset of 19 Where's Waldo maps, divided into labeled tiles, and expanding it with additional maps to train a model to identify Waldo. Initial testing showed low detection rates, especially in complex scenes, due to the abundance of non-Waldo elements. Adjustments were made by manually removing potential false-positive lookalikes from training data, which improved accuracy but still faced challenges with busy scenes and unexpected false positives. The project highlighted the iterative nature of machine learning, emphasizing the need for more balanced and Waldo-positive examples to enhance model accuracy. Users interested in similar machine learning projects can explore these techniques through Clarifai's Custom Training platform.