AI models
ResNeXt-101 (64×4d)
Training compute
1.2×10¹⁹ FLOP
Parameters
83M
Published
Nov 16, 2016
ResNeXt-101 (64×4d) is an AI model developed by University of California San Diego and Facebook (United States), first published in November 2016. It works in the vision domain, on tasks such as image classification.
Training it took an estimated 1.2×10¹⁹ FLOP of compute (estimation method: third-party estimation). The model has 83,000,000 parameters. It was trained on roughly 1.3M datapoints.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 11,620 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of California San Diego, Facebook
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 1.2×10¹⁹ FLOP
- Compute estimation method
- Third-party estimation
- Parameters
- 83,000,000
- Dataset size
- 1.3M
- Numerical format
- FP32
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 11,620
- Epoch confidence
- Confident
More from University of California San Diego,Facebook
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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