AI models
GoogLeNet / InceptionV1
Training compute
1.5×10¹⁸ FLOP
Parameters
6.8M
Published
Sep 17, 2014
GoogLeNet / InceptionV1 is an AI model developed by Google, University of Michigan and University of North Carolina (United States), first published in September 2014. It works in the vision domain, on tasks such as image classification.
Training it took an estimated 1.5×10¹⁸ FLOP of compute (estimation method: third-party estimation). The model has 6,797,700 parameters. It was trained on roughly 571.4B datapoints.
The reference paper has 47,229 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Google, University of Michigan, University of North Carolina
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 1.5×10¹⁸ FLOP
- Compute estimation method
- Third-party estimation
- Parameters
- 6,797,700
- Dataset size
- 571.4B
- Citations
- 47,229
- 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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