AI models
Detic
Training compute
2.3×10¹⁹ FLOP
Parameters
88M
Published
Jan 7, 2022
Detic is an AI model developed by Meta AI and University of Texas at Austin (United States), first published in January 2022. It works in the vision domain, on tasks such as object detection and image classification.
Training it took an estimated 2.3×10¹⁹ FLOP of compute (estimation method: hardware). The model has 88,000,000 parameters. Training ran on 32 NVIDIA V100 for about 24 hours. The compute alone is estimated at $191 in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 841 citations. Epoch AI rates the confidence of this record as speculative.
Full record
- Organization
- Meta AI, University of Texas at Austin
- Country of organization
- United States
- Domain
- Vision
- Task
- Object detection, Image classification
- Training compute
- 2.3×10¹⁹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 88,000,000
- Training hardware
- NVIDIA V100
- Chips used
- 32
- Training time
- 24 h
- Chip-hours
- 768
- Training power draw
- 19.3 kW
- Training cost (2023 USD)
- $191
- Numerical format
- FP16
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 841
- Epoch confidence
- Speculative
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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