ANN Eye Tracker
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
- 29 November 1993
- Authors
- S. Baluja, D. Pomerleau
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Miscellaneous image analysis
- Approach
- Supervised
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
- 5.6K
- Training data
- 4,000 tokens
- Epochs
- 260
15*15*20+20+50*10*2+100=5620 Hidden layer is split, 15*15 image input, 2*50 output neurons (see Figure 2) Hidden size up to 20 neurons ("This architecture was used with varying numbers of hidden units in the single, divided, hidden layer; experiments with 10, 16 and 20 hidden units were performed. ")
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
- 1.8 × 10¹⁰ FLOP
- How it was established
- Operation counting
2*5620*3*520000=17534400000 Training examples: 2000*260=520000 "As mentioned before, 2000 image/position pairs were gathered for training" "All of the networks described in this paper are trained with the same parameters for 260 epochs"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 1 hours
"Training the 8x2 hidden layer network using the 15x40 input retina, with 2000 images, takes approximately 30-40 minutes on a Sun SPARC 10 machine. "
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
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Non-Intrusive Gaze Tracking Using Artificial Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
ANN Eye Tracker was published by its authors, during November 1993.
It works in the domain of Vision, and is recorded as performing the task of miscellaneous image analysis.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took a computation budget of roughly 1.8 × 10¹⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 4,000 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
ANN Eye Tracker — common questions
ANN Eye Tracker— 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.
ANN Eye Tracker— how many parameters does it have?
It has a parameter count of 5.6K. 15*15*20+20+50*10*2+100=5620 Hidden layer is split, 15*15 image input, 2*50 output neurons (see Figure 2) Hidden size up to 20 neurons ("This architecture was used with varying numbers of hidden units in the single, divided, hidden layer; experiments with 10, 16 and 20 hidden units were performed. "). 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.
ANN Eye Tracker— when was it released?
It was published in November 1993. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ANN Eye Tracker— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of miscellaneous image analysis. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
ANN Eye Tracker— how much compute was used to train it?
Training consumed around 1.8 × 10¹⁰ 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.
ANN Eye Tracker— 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.