LMICA
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 December 2004
- Authors
- Yoshitatsu Matsuda, K. Yamaguchi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
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
- 4.1M
- Training data
- 100,000 tokens
64*64*1000=4096000 "100000 samples of natural scenes of 64 × 64 pixels were given as X" "LMICA was carried out in 1000 layers"
"100000 samples of natural scenes of 64 × 64 pixels were given as X"
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
- 2.8 × 10¹⁵ FLOP
- How it was established
- Hardware
69*60*60*8*2800000000*0.5=2782080000000000=2.78e15 "it consumed about 69 hours with Intel 2.8GHz CPU" - Assuming they used an Intel Pentium 4 processor with 8 FLOP/cycle (https://en.wikipedia.org/wiki/FLOPS)
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
- Training cost,Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Linear Multilayer Independent Component Analysis for Large Natural Scenes
- Last updated
- 28 November 2025
What the numbers mean
About this model
LMICA was published by its authors, in December 2004.
It works in Vision, and is recorded as doing object detection.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took roughly 2.8 × 10¹⁵ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 100,000 tokens went into training it.
The reason it appears in this catalogue at all is training cost,Historical significance.
Answers
LMICA — common questions
How much compute was used to train LMICA?
Around 2.8 × 10¹⁵ 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.
What GPU do I need to run LMICA?
None. LMICA 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.
Is LMICA open source?
The licensing for LMICA was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does LMICA have?
LMICA has 4.1M parameters. 64*64*1000=4096000 "100000 samples of natural scenes of 64 × 64 pixels were given as X" "LMICA was carried out in 1000 layers". 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.
When was LMICA released?
LMICA was published in December 2004. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is LMICA used for?
LMICA works in Vision, and is recorded as handling object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.