AI models
GELU for CIFAR-10
Training compute
7.4×10¹¹ FLOP
Parameters
9.9K
Published
Jun 6, 2023
GELU for CIFAR-10 is an AI model developed by University of California (UC) Berkeley and Toyota Technological Institute at Chicago (United States), first published in June 2023. It works in the vision domain, on tasks such as image classification.
Training it took an estimated 7.4×10¹¹ FLOP of compute (estimation method: operation counting). The model has 9,888 parameters. It was trained on roughly 50K datapoints. Training ran on NVIDIA GeForce GTX TITAN X.
Epoch AI rates the confidence of this record as speculative.
Full record
- Organization
- University of California (UC) Berkeley, Toyota Technological Institute at Chicago
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 7.4×10¹¹ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 9,888
- Dataset size
- 50K
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Epoch confidence
- Speculative
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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