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To efficiently train neural networks for tasks like animal image recognition, a staggering volume of both images and their corresponding metadata is required. The challenge intensifies when considering the scale needed — about 30-40 million metadata units for just 10 million images. An annotator can produce 135 metadata units in a 4.5-hour workday, leading to 2,835 units over a typical 21-workday month. The monumental task of generating 35 million metadata units would take a single person over a millennium to complete. However, a team of 100 could manage it in a little over 10 years, and 1,000 annotators could dramatically reduce this time to just one year.
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