YOLOv11
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
- Huddersfield University
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 23 October 2024
- Authors
- Rahima Khanam, Muhammad Hussain
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
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
- 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
- 2,604
Sources
Where this record came from and when it was last checked.
- Reference
- YOLOv11: An Overview of the Key Architectural Enhancements
- Last updated
- 25 May 2026
What the numbers mean
What this model is
YOLOv11 was published by Huddersfield University, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2024. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of object detection.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
YOLOv11 — common questions
YOLOv11— who created it?
It was published by Huddersfield University, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia.
YOLOv11— when was it released?
It was published in October 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
YOLOv11— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
YOLOv11— 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.
YOLOv11— 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.
YOLOv11— how many parameters does it have?
No parameter count has been published for it, 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.