Multi-scale Dilated CNN
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
- Princeton University,Intel Labs
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 23 November 2015
- Authors
- Fisher Yu, Vladlen Koltun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image segmentation
- 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.
- Training data
- tokens
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
- Record confidence
- Unknown
- Citations
- 9,368
Sources
Where this record came from and when it was last checked.
- Reference
- Multi-Scale Context Aggregation by Dilated Convolutions
- Last updated
- 25 May 2026
What the numbers mean
About this model
Multi-scale Dilated CNN was published by Princeton University,Intel Labs, in United States of America, in November 2015. It comes out of academia,Industry.
It works in Vision, and is recorded as doing image segmentation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Its inclusion criterion is highly cited.
Answers
Multi-scale Dilated CNN — common questions
What GPU do I need to run Multi-scale Dilated CNN?
None. Multi-scale Dilated CNN 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 Multi-scale Dilated CNN open source?
The licensing for Multi-scale Dilated CNN 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 Multi-scale Dilated CNN have?
No parameter count has been published for Multi-scale Dilated CNN, which is why no memory or speed figure appears on this page.
Who created Multi-scale Dilated CNN?
Multi-scale Dilated CNN was published by Princeton University,Intel Labs, based in United States of America, categorised as academia,Industry.
When was Multi-scale Dilated CNN released?
Multi-scale Dilated CNN was published in November 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.
What is Multi-scale Dilated CNN used for?
Multi-scale Dilated CNN works in Vision, and is recorded as handling image segmentation. 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.
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.