AI models
InternImage
Training compute
2.4×10²¹ FLOP
Parameters
1.1B
Published
Nov 10, 2022
InternImage is an AI model developed by Shanghai AI Lab, Tsinghua University, Nanjing University, SenseTime and Chinese University of Hong Kong (CUHK) (China and Hong Kong), first published in November 2022. It works in the vision domain, on tasks such as image classification, object detection and image segmentation.
Training it took an estimated 2.4×10²¹ FLOP of compute (estimation method: operation counting). The model has 1,080,000,000 parameters. It was trained on roughly 83.7B datapoints.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 1,087 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Shanghai AI Lab, Tsinghua University, Nanjing University, SenseTime, Chinese University of Hong Kong (CUHK)
- Country of organization
- China, Hong Kong
- Domain
- Vision
- Task
- Image classification, Object detection, Image segmentation
- Training compute
- 2.4×10²¹ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 1,080,000,000
- Dataset size
- 83.7B
- Numerical format
- FP16
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 1,087
- 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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