AI models
R-FCN
Training compute
7.2×10¹⁷ FLOP
Published
Jun 21, 2016
R-FCN is an AI model developed by Tsinghua University and Microsoft Research (China and United States), first published in June 2016. It works in the vision domain, on tasks such as object detection.
Training it took an estimated 7.2×10¹⁷ FLOP of compute (estimation method: hardware). It was trained on roughly 10.6M datapoints.
The reference paper has 6,003 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Tsinghua University, Microsoft Research
- Country of organization
- China, United States
- Domain
- Vision
- Task
- Object detection
- Training compute
- 7.2×10¹⁷ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 10.6M
- Numerical format
- FP32
- Citations
- 6,003
- 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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