ResNet-110 (CIFAR-10)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 10 December 2015
- Authors
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.7M
- Training data
- 50,000 tokens
Table 6
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Citations
- 228,517
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Residual Learning for Image Recognition
- Last updated
- 25 May 2026
What the numbers mean
Background
ResNet-110 (CIFAR-10) was published by Microsoft, in the country recorded as United States of America, during December 2015. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on a corpus of about 50,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
ResNet-110 (CIFAR-10) — common questions
ResNet-110 (CIFAR-10)— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
ResNet-110 (CIFAR-10)— how many parameters does it have?
It has a parameter count of 1.7M. Table 6. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
ResNet-110 (CIFAR-10)— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
ResNet-110 (CIFAR-10)— when was it released?
It was published in December 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ResNet-110 (CIFAR-10)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
ResNet-110 (CIFAR-10)— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.