Visualizing CNNs
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
- New York University (NYU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 12 November 2013
- Authors
- MD Zeiler, R Fergus
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.
- Training data
- 7,680,000 tokens
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
- 5.3 × 10¹⁷ FLOP
- How it was established
- Hardware,Third-party estimation
1 GPU * 12 days * 1.54 TFLOPS/GTX 580 * 0.33 utilization = 532 PF = 0.0062 pfs-days Source: https://openai.com/blog/ai-and-compute "We stopped training after 70 epochs, which took around 12 days on a single GTX580 GPU"
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 GeForce GTX 580
- Compute cost
- $13
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
- 17,026
Sources
Where this record came from and when it was last checked.
- Reference
- Visualizing and Understanding Convolutional Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
Visualizing CNNs was published by New York University (NYU), in the country recorded as United States of America, during November 2013. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 5.3 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX 580. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 7,680,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Visualizing CNNs — common questions
Visualizing CNNs— 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.
Visualizing CNNs— 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.
Visualizing CNNs— 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.
Visualizing CNNs— who created it?
It was published by New York University (NYU), based in United States of America, an organisation categorised as academia.
Visualizing CNNs— when was it released?
It was published in November 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Visualizing CNNs— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. 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.
Visualizing CNNs— how much compute was used to train it?
Training consumed around 5.3 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX 580. 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.
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.