AI models
ResNet-RS
Training compute
1.8×10²² FLOP
Parameters
192M
Published
Mar 13, 2021
ResNet-RS is an AI model developed by Google Brain and University of California (UC) Berkeley (United States), first published in March 2021. It works in the vision domain, on tasks such as image classification.
Training it took an estimated 1.8×10²² FLOP of compute (estimation method: operation counting). The model has 192,000,000 parameters. Training ran on Google TPU v3.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 358 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Google Brain, University of California (UC) Berkeley
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 1.8×10²² FLOP
- Compute estimation method
- Operation counting
- Parameters
- 192,000,000
- Training hardware
- Google TPU v3
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 358
- Epoch confidence
- Confident
More from Google Brain,University of California (UC) Berkeley
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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