OR-WideResNet
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
- Duke University,University of Chinese Academy of Sciences
- Organisation type
- Academia,Academia
- Country
- United States of America, China
- Published
- 7 January 2017
- Authors
- Yanzhao Zhou, Qixiang Ye, Qiang Qiu and Jianbin Jiao
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 18.2M
- Training data
- 50,000 tokens
18.2M for largest OR-WideResNet model.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla K80
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 285
"In Sec. 4.3, we upgrade the VGG [38], ResNet [18], and the WideResNet [45] to ORNs, and train them on CIFAR10 and CIFAR100 [22], showing the state-of-the-art performance on the natural image classification task."
Sources
Where this record came from and when it was last checked.
- Reference
- Oriented Response Networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
OR-WideResNet was published by Duke University,University of Chinese Academy of Sciences, in United States of America, in January 2017. It comes out of academia,Academia.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training set ran to roughly 50,000 tokens.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
OR-WideResNet — common questions
How many parameters does OR-WideResNet have?
OR-WideResNet has 18.2M parameters. 18.2M for largest OR-WideResNet model. 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.
Who created OR-WideResNet?
OR-WideResNet was published by Duke University,University of Chinese Academy of Sciences, based in United States of America, categorised as academia,Academia.
When was OR-WideResNet released?
OR-WideResNet was published in January 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is OR-WideResNet used for?
OR-WideResNet works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run OR-WideResNet?
None. OR-WideResNet 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.
Is OR-WideResNet open source?
The licensing for OR-WideResNet was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.