YOLO
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
- University of Washington,Allen Institute for AI,Facebook AI Research
- Organisation type
- Academia,Research collective,Industry
- Country
- United States of America, France
- Published
- 8 June 2015
- Authors
- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
- 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
- 271.7M
- Training data
- tokens
Calculation based on figure 3 of the paper: 7 * 7 * 3 * 64 + 3 * 3 * 64 * 192 + 1 * 1 * 192 * 128 + 3 * 3 * 128 * 256 + 1 * 1 * 256 * 256 + 3 * 3 * 256 * 512 + 4 * (1 * 1 * 512 * 256 + 3 * 3 * 256 * 512) + 1 * 1 * 512 * 512 + 3 * 3 * 512 * 1024 + 2 * (1 * 1 * 1024 * 512 + 3 * 3 * 512 * 1024) + 4 * (3 * 3 * 1024 * 1024) + 7 * 7 * 1024 * 4096 + 4096 * 7 * 7 * 30
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
- 45,325
Sources
Where this record came from and when it was last checked.
- Reference
- You Only Look Once: Unified, Real-Time Object Detection
- Last updated
- 25 May 2026
What the numbers mean
About this model
YOLO was published by University of Washington,Allen Institute for AI,Facebook AI Research, in the country recorded as United States of America, during June 2015. The category the publisher falls under is academia,Research collective,Industry.
It works in the domain of Vision, and is recorded as performing the task of object detection.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
YOLO — common questions
YOLO— when was it released?
It was published in June 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.
YOLO— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. These are the areas it was designed around; they describe intent rather than a hard boundary.
YOLO— 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.
YOLO— 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.
YOLO— how many parameters does it have?
It has a parameter count of 271.7M. Calculation based on figure 3 of the paper: 7 * 7 * 3 * 64 + 3 * 3 * 64 * 192 + 1 * 1 * 192 * 128 + 3 * 3 * 128 * 256 + 1 * 1 * 256 * 256 + 3 * 3 * 256 * 512 + 4 * (1 * 1 * 512 * 256 + 3 * 3 * 256 * 512) + 1 * 1 * 512 * 512 + 3 * 3 * 512 * 1024 + 2 * (1 * 1 * 1024 * 512 + 3 * 3 * 512 * 1024) + 4 * (3 * 3 * 1024 * 1024) + 7 * 7 * 1024 * 4096 + 4096 * 7 * 7 * 30. 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.
YOLO— who created it?
It was published by University of Washington,Allen Institute for AI,Facebook AI Research, based in United States of America, an organisation categorised as academia,Research collective,Industry.
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.