AI models
AlphaX-1
Training compute
8.9×10¹⁷ FLOP
Parameters
5.4M
Published
Oct 2, 2019
AlphaX-1 is an AI model developed by Facebook AI Research and Brown University (United States and France), first published in October 2019. It works in the vision domain, on tasks such as neural architecture search for computer vision, image classification, object detection and image captioning.
Training it took an estimated 8.9×10¹⁷ FLOP of compute. The model has 5,400,000 parameters. It was trained on roughly 61.3M datapoints. Training ran on NVIDIA GeForce GTX 1080 Ti.
Access: Unreleased. Its weights are not openly released. The reference paper has 100 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Facebook AI Research, Brown University
- Country of organization
- United States, France
- Domain
- Vision
- Task
- Neural architecture search for computer vision, Image classification, Object detection, Image captioning
- Training compute
- 8.9×10¹⁷ FLOP
- Parameters
- 5,400,000
- Dataset size
- 61.3M
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 100
- 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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