AI models
SqueezeNet
Parameters
1.2M
Published
Feb 24, 2016
SqueezeNet is an AI model developed by DeepScale, University of California (UC) Berkeley and Stanford University (United States), first published in February 2016. It works in the vision domain, on tasks such as image classification.
Epoch AI has no training-compute estimate for this model. The model has 1,200,000 parameters. It was trained on roughly 1.3M datapoints.
The reference paper has 8,366 citations.
Full record
- Organization
- DeepScale, University of California (UC) Berkeley, Stanford University
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Parameters
- 1,200,000
- Dataset size
- 1.3M
- Numerical format
- FP32
- Citations
- 8,366
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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