AI models
SPPNet
Training compute
3.4×10¹⁸ FLOP
Published
Jun 18, 2014
SPPNet is an AI model developed by Microsoft, Xi’an Jiaotong University and University of Science and Technology of China (USTC) (United States and China), first published in June 2014. It works in the vision domain, on tasks such as image classification. It counts among the frontier models: the systems trained with the most compute of their moment.
Training it took an estimated 3.4×10¹⁸ FLOP of compute (estimation method: hardware). It was trained on roughly 1.3M datapoints. Training ran on NVIDIA GeForce GTX TITAN for about 672 hours. The compute alone is estimated at $52 in 2023 dollars.
The reference paper has 12,524 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Microsoft, Xi’an Jiaotong University, University of Science and Technology of China (USTC)
- Country of organization
- United States, China
- Domain
- Vision
- Task
- Image classification
- Training compute
- 3.4×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 1.3M
- Training hardware
- NVIDIA GeForce GTX TITAN
- Training time
- 672 h
- Training cost (2023 USD)
- $52
- Numerical format
- FP32
- Citations
- 12,524
- Epoch confidence
- Confident
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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