Diabetic Retinopathy Detection Net
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
- UT Austin,University of California (UC) Berkeley,Google
- Organisation type
- Academia,Academia,Industry
- Country
- United States of America
- Published
- 13 December 2016
- Authors
- V Gulshan, L Peng, M Coram, MC Stumpe, D Wu
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.
- Training data
- 128,175 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 3,540
Sources
Where this record came from and when it was last checked.
- Reference
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
- Last updated
- 28 November 2025
What the numbers mean
About this model
Diabetic Retinopathy Detection Net was published by UT Austin,University of California (UC) Berkeley,Google, in United States of America, in December 2016. The organisation is categorised as academia,Academia,Industry.
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
Around 128,175 tokens went into training it.
Answers
Diabetic Retinopathy Detection Net — common questions
Who created Diabetic Retinopathy Detection Net?
Diabetic Retinopathy Detection Net was published by UT Austin,University of California (UC) Berkeley,Google, based in United States of America, categorised as academia,Academia,Industry.
When was Diabetic Retinopathy Detection Net released?
Diabetic Retinopathy Detection Net was published in December 2016. 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 Diabetic Retinopathy Detection Net used for?
Diabetic Retinopathy Detection Net 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 Diabetic Retinopathy Detection Net?
None. Diabetic Retinopathy Detection Net 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 Diabetic Retinopathy Detection Net open source?
The licensing for Diabetic Retinopathy Detection Net was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Diabetic Retinopathy Detection Net have?
No parameter count has been published for Diabetic Retinopathy Detection Net, which is why no memory or speed figure appears on this page.
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.