MSRA (C, PReLU)
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 Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 6 February 2015
- 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
- 87M
- Training data
- 1,280,000 tokens
I used the architecture in table 3 I ignored biases, and assumed a SPP bin size of 256 3*7*7*96+96*3*3*384+384*3*3*384*5+384*3*3*768+768*3*3*768*5+768*3*3*896+896*3*3*896*5+896*(7*7+3*3+2*2+1)*4096+4096*4096+4096*1000=330581792
"We perform the experiments on the 1000-class ImageNet 2012 dataset", paper; ImageNet 2012 train set size from https://huggingface.co/datasets/imagenet-1k
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.4 × 10¹⁹ FLOP
- How it was established
- Hardware
"training C on eight K40 GPUs, takes about 3-4 weeks" 0.33 util rate (From Imagenet paper-data, Besiroglu et al., forthcoming)
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla K40t
- Wall-clock time
- 588 hours (24.5 days)
- Compute cost
- $1,394
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Confident
- Citations
- 20,475
Sources
Where this record came from and when it was last checked.
- Reference
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Last updated
- 25 May 2026
What the numbers mean
What this model is
MSRA (C, PReLU) was published by Microsoft Research, in the country recorded as United States of America, during February 2015. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 2.4 × 10¹⁹ FLOP, on hardware recorded as NVIDIA Tesla K40t. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 1,280,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
MSRA (C, PReLU) — common questions
MSRA (C, PReLU)— who created it?
It was published by Microsoft Research, based in United States of America, an organisation categorised as industry.
MSRA (C, PReLU)— when was it released?
It was published in February 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.
MSRA (C, PReLU)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MSRA (C, PReLU)— how much compute was used to train it?
Training consumed around 2.4 × 10¹⁹ FLOP, on hardware recorded as NVIDIA Tesla K40t. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
MSRA (C, PReLU)— 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.
MSRA (C, PReLU)— 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.
MSRA (C, PReLU)— how many parameters does it have?
It has a parameter count of 87M. I used the architecture in table 3 I ignored biases, and assumed a SPP bin size of 256 3*7*7*96+96*3*3*384+384*3*3*384*5+384*3*3*768+768*3*3*768*5+768*3*3*896+896*3*3*896*5+896*(7*7+3*3+2*2+1)*4096+4096*4096+4096*1000=330581792. 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.
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.