SexNet classification
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.
- Published
- 1 October 1990
- Authors
- B. Golomb, D. T. Lawrence, T. Sejnowski
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
- 1.6K
- Training data
- 80 tokens
Largest classification model: 40*40 + 40=1640 (Figure 2)
“Each training on a different 80 faces, leaving a distinct set of 10 untrained faces for testing”
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
- Historical significance,Highly cited
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- SEXNET: A Neural Network Identifies Sex From Human Faces
- Last updated
- 28 November 2025
What the numbers mean
Background
SexNet classification was published by its authors, during October 1990.
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
The training set ran to roughly 80 tokens of text.
Its inclusion criterion: historical significance,Highly cited.
Answers
SexNet classification — common questions
SexNet classification— 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.
SexNet classification— how many parameters does it have?
It has a parameter count of 1.6K. Largest classification model: 40*40 + 40=1640 (Figure 2). 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.
SexNet classification— when was it released?
It was published in October 1990. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SexNet classification— 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.
SexNet classification— 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.
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.