AI models
Inception v3
Training compute
10²⁰ FLOP
Parameters
23.6M
Published
Dec 2, 2015
Inception v3 is an AI model developed by Google and University College London (UCL) (United States and United Kingdom), first published in December 2015. 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 10²⁰ FLOP of compute (estimation method: third-party estimation). The model has 23,626,728 parameters. It was trained on roughly 1.2M datapoints. The compute alone is estimated at $1K in 2023 dollars.
The reference paper has 30,915 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Google, University College London (UCL)
- Country of organization
- United States, United Kingdom
- Domain
- Vision
- Task
- Image classification
- Training compute
- 10²⁰ FLOP
- Compute estimation method
- Third-party estimation
- Parameters
- 23,626,728
- Dataset size
- 1.2M
- Training cost (2023 USD)
- $1K
- Citations
- 30,915
- Epoch confidence
- Likely
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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