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Computer Science > Machine Learning

arXiv:2404.00498 (cs)
[Submitted on 30 Mar 2024 (v1), last revised 5 Apr 2024 (this version, v2)]

Title:94% on CIFAR-10 in 3.29 Seconds on a Single GPU

Authors:Keller Jordan
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Abstract:CIFAR-10 is among the most widely used datasets in machine learning, facilitating thousands of research projects per year. To accelerate research and reduce the cost of experiments, we introduce training methods for CIFAR-10 which reach 94% accuracy in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds, when run on a single NVIDIA A100 GPU. As one factor contributing to these training speeds, we propose a derandomized variant of horizontal flipping augmentation, which we show improves over the standard method in every case where flipping is beneficial over no flipping at all. Our code is released at this https URL.
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2404.00498 [cs.LG]
  (or arXiv:2404.00498v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2404.00498

Submission history

From: Keller Jordan [view email]
[v1] Sat, 30 Mar 2024 23:42:23 UTC (1,006 KB)
[v2] Fri, 5 Apr 2024 00:09:00 UTC (1,888 KB)
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