Datasets › Sim10k

Sim10k

Introduced by Matthew Johnson-Roberson et al. in Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?1 Jan 2017 archive 2025-07-28

SIM10k is a synthetic dataset containing 10,000 images, which is rendered from the video game Grand Theft Auto V (GTA5).

Source: Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation Image Source: https://arxiv.org/pdf/1610.01983.pdf

Benchmarks archive 2025-07-28

All 3 leaderboards 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.

Papers archive 2025-07-28

10 shown of 10 papers 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 92. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Custom (non-commercial)

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • SIM10K to Cityscapes
  • SIM10K to BDD100K
  • Sim10k

3 variant names, as the archive lists them.

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