Self-Attention and Convolutional Layers
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
- Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 8 November 2019
- Authors
- Jean-Baptiste Cordonnier, Andreas Loukas & Martin Jaggi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 29.5M
- Training data
- tokens
- Epochs
- 300
from Table 1
size of CIFAR-10
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
- 6.8 × 10¹⁷ FLOP
- How it was established
- Operation counting
(15e9) * (300) * (50000) * 3 = 6.75e+17 (inference compute) * (epochs) * (dataset size) * (constant to account for backpropagation) epochs from appendix B table 2 inference compute from table 1
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
Apache 2.0 for train/eval code: https://github.com/epfml/attention-cnn data is CIFAR which doesn't have a clear license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 630
Sources
Where this record came from and when it was last checked.
- Reference
- On the Relationship between Self-Attention and Convolutional Layers
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Self-Attention and Convolutional Layers was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL), in the country recorded as Switzerland, during November 2019. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of 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
Producing it required arithmetic totalling around 6.8 × 10¹⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Self-Attention and Convolutional Layers — common questions
Self-Attention and Convolutional Layers— who created it?
It was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL), based in Switzerland, an organisation categorised as academia.
Self-Attention and Convolutional Layers— when was it released?
It was published in November 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Self-Attention and Convolutional Layers— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Self-Attention and Convolutional Layers— how much compute was used to train it?
Training consumed around 6.8 × 10¹⁷ FLOP. 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.
Self-Attention and Convolutional Layers— 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.
Self-Attention and Convolutional Layers— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Self-Attention and Convolutional Layers— how many parameters does it have?
It has a parameter count of 29.5M. from Table 1. 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.