AI models
Mono3D++
Training compute
4.9×10¹⁸ FLOP
Published
Jan 11, 2019
Mono3D++ is an AI model developed by University of California Los Angeles (UCLA) and Megvii Inc (United States and China), first published in January 2019. It works in the 3d modeling and vision domain, on tasks such as 3d segmentation and object detection.
Training it took an estimated 4.9×10¹⁸ FLOP of compute (estimation method: hardware). Training ran on 4 NVIDIA GeForce GTX TITAN X for about 168 hours.
Access: Unreleased. Its weights are not openly released. The reference paper has 135 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of California Los Angeles (UCLA), Megvii Inc
- Country of organization
- United States, China
- Domain
- 3D modeling, Vision
- Task
- 3D segmentation, Object detection
- Training compute
- 4.9×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 4
- Training time
- 168 h
- Chip-hours
- 672
- Training power draw
- 2.1 kW
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 135
- 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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