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How Lyft uses Weights & Biases

4.3x

“we speeded up EEF training by 4.3x and also improved model accuracies”

As published on wandb.ai, October 2023. Captured by usedby on Oct 8, 2026.

What happened

Lyft Level 5 used Weights & Biases, alongside its internal ML framework Jadoo, to run experiments on end-to-end camera-lidar fusion models for 3D detection in autonomous vehicles. The experiments compared fusion strategies, model capacity, data augmentation, and training speed optimizations.

Summary written by usedby from the source page, in English. The figures are those of Weights & Biases and Lyft, not ours.

  • 1.2x“We used the automatic mixed-precision from PyTorch and further speeded up training by 1.2x, reduced GPU memory footprint without regressing model accuracies.”
  • 2.3x“This reduced compute and data loading time and led to 2.3x faster training.”
  • 2x“This further led to 2x faster training than using full-res images without much accuracy regression.”
  • 1.5%“we observed better accuracies (shown below: +1.5% AP@0.5 for pedestrians).”

With the help from both image dropout and half-res images, we speeded up EEF training by 4.3x and also improved model accuracies.

From the page. wandb.ai, captured Oct 8, 2026

What the story claims, and what we checked

We compared the story with its live page on Oct 8, 2026.

  • The figure: 4.3xCheckedPrinted word for word on the page, near the name of Lyft.
  • The passage quoted aboveCheckedCopied word for word from the page, near the name of Lyft.
  • The numbers in our summaryCheckedEach one is printed on the page.
  • The publication dateCheckedRead from the page’s own metadata, never guessed.
  • Lyft uses Weights & BiasesCheckedConfirmed line. Latest check across sources: Oct 8, 2026.
  • The result itselfNot checkedWe quote it; we did not measure it.

Same company, same tool or same industry.

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