AI models
ResNet-152 + ObjectNet
Training compute
1.9×10¹⁹ FLOP
Parameters
38M
Published
Sep 6, 2019
ResNet-152 + ObjectNet is an AI model developed by Massachusetts Institute of Technology (MIT) (United States), first published in September 2019. It works in the vision domain, on tasks such as object recognition.
Training it took an estimated 1.9×10¹⁹ FLOP of compute (estimation method: hardware). The model has 38,000,000 parameters. It was trained on roughly 50K datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 2,393 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Massachusetts Institute of Technology (MIT)
- Country of organization
- United States
- Domain
- Vision
- Task
- Object recognition
- Training compute
- 1.9×10¹⁹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 38,000,000
- Dataset size
- 50K
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 2,393
- Epoch confidence
- Confident
More from Massachusetts Institute of Technology (MIT)
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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