Datasets › JFT-300M
JFT-300M
JFT-300M is an internal Google dataset used for training image classification models. Images are labeled using an algorithm that uses complex mixture of raw web signals, connections between web-pages and user feedback. This results in over one billion labels for the 300M images (a single image can have multiple labels). Of the billion image labels, approximately 375M are selected via an algorithm that aims to maximize label precision of selected images.
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Image Classification | JFT-300M | V-MoE-H/14 (Every-2) prec@1 60.62 | Scaling Vision with Sparse Mixture of Experts | google-research/vmoe | 4 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 123. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Scaling Vision with Sparse Mixture of Experts | 1 | 4 | 10 Jun 2021 | ran 1 of 1 samples (0 unverified) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Private (not publicly available)
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- JFT-300M
1 variant name, as the archive lists them.
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