JPMAX
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
- 2 December 1994
- Authors
- Suzanna Becker
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image representation
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
- 4.4K
- Training data
- 1,500 tokens
Inputs are 12x12 pixels (Figure 3). They first train the architecture in Figure 2 a), then freeze it and train the additional layer in Figure 3 b). Figure 2 a): 2*(12*12*15) + 2*15 = 4,350 Figure 2 b): 2*(12*12*15) + 2*15 + 2*(15*3) + 2*3 = 4,446
“Figure 3: 10 of the 1500 training patterns”
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
- 8.1 × 10⁷ FLOP
- How it was established
- Operation counting
Training 2a): "The learning took about 3000 iterations of steepest descent" Assuming each iteration refers to a single image. 6 * 4446 * 3000 = 80,028,000 Training 2b): "While keeping the first layer of weights frozen, this network was trained using exactly the same cost function as the first layer for about 30 iterations using a gradient-based learning method." 6 * 4446 * 30 = 800,280 Total: 80,828,280
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
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- JPMAX: Learning to Recognize Moving Objects as a Model-fitting Problem
- Last updated
- 28 November 2025
What the numbers mean
About this model
JPMAX was published by its authors, during December 1994.
It works in the domain of Vision, and is recorded as performing the task of image representation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 8.1 × 10⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 1,500 tokens of text.
Its inclusion criterion: historical significance.
Answers
JPMAX — common questions
JPMAX— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image representation. 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.
JPMAX— how much compute was used to train it?
Training consumed around 8.1 × 10⁷ 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.
JPMAX— 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.
JPMAX— 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.
JPMAX— how many parameters does it have?
It has a parameter count of 4.4K. Inputs are 12x12 pixels (Figure 3). They first train the architecture in Figure 2 a), then freeze it and train the additional layer in Figure 3 b). Figure 2 a): 2*(12*12*15) + 2*15 = 4,350 Figure 2 b): 2*(12*12*15) + 2*15 + 2*(15*3) + 2*3 = 4,446. 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.
JPMAX— when was it released?
It was published in December 1994. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.