ResNet-1001
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 17 September 2016
- Authors
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- 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
- 10.2M
- Training data
- 50,000 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
- Citations
- 10,822
Sources
Where this record came from and when it was last checked.
- Reference
- Identity Mappings in Deep Residual Networks
- Last updated
- 1 January 2026
What the numbers mean
Background
ResNet-1001 was published by Microsoft, in United States of America, in September 2016. It comes out of industry.
It works in Vision, and is recorded as doing image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Around 50,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
ResNet-1001 — common questions
How many parameters does ResNet-1001 have?
ResNet-1001 has 10.2M parameters. 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 ResNet-1001?
ResNet-1001 was published by Microsoft, based in United States of America, categorised as industry.
When was ResNet-1001 released?
ResNet-1001 was published in September 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 ResNet-1001 used for?
ResNet-1001 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 ResNet-1001?
None. ResNet-1001 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 ResNet-1001 open source?
The licensing for ResNet-1001 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.