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#crowdsourcing1#data-quality2#mechanical-turk1
Creating tens of millions of high‑quality labeled images required massive crowdsourcing via Mechanical Turk
  • The ImageNet team estimated a need for “tens of millions of high‑quality images across every possible diverse dimension.”
  • Manual labeling by staff was infeasible; they turned to Amazon’s Mechanical Turk to harness a global workforce.
  • This approach allowed parallel processing of billions of images, ultimately distilling them down to ~15 million high‑quality examples.
  • The scale of the effort demonstrated that modern AI datasets rely on large‑scale human annotation pipelines.
  • It also highlighted the importance of designing tasks that can be reliably completed by non‑expert workers.
Dr. Fei‑Fei LiTim Ferriss Show00:33:21

Supporting quotes

Quote
00:33:21
“We needed tens of millions of high quality images across every possible diverse dimension.” — Dr. Fei‑Fei Li
Quote
00:36:04
“We labeled billions of images and distilled it down to 15 million high quality images.” — Dr. Fei‑Fei Li

From this concept

Crowdsourcing & Data Quality

Labeling tens of millions of images required innovative crowdsourcing strategies, rigorous quality controls, and a shift away from traditional labor-intensive labeling approaches.

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