LCNP LabelMe
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
- University of Bonn
- Organisation type
- Academia
- Country
- Germany
- Published
- 22 November 2009
- Authors
- Rafael Uetz, Sven Behnke
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object recognition
- 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
- 13.7M
- Training data
- 40,000 tokens
- Epochs
- 1,000
Locally connected: 128*128*4*4*4*7 + 64*64*8*4*4*4 + 32*32*16*4*4*8 + 16*16*32*4*4*16=13631488 Classification head: 16*16*32*12=98304 Total: 13631488+98304=13729792 "five regular layers with the dimensions 256×256, 128×128, . . ., 16×16." "size of the receptive field to be 4 × 4 neurons"
Table 1
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
- 3.3 × 10¹⁵ FLOP
- How it was established
- Operation counting
2*13729792*3*40000*1000=3295150080000000
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
- Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Large-scale object recognition with CUDA-accelerated hierarchical neural networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
LCNP LabelMe was published by University of Bonn, in the country recorded as Germany, during November 2009. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of object recognition.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took a computation budget of roughly 3.3 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 40,000 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
LCNP LabelMe — common questions
LCNP LabelMe— 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.
LCNP LabelMe— how many parameters does it have?
It has a parameter count of 13.7M. Locally connected: 128*128*4*4*4*7 + 64*64*8*4*4*4 + 32*32*16*4*4*8 + 16*16*32*4*4*16=13631488 Classification head: 16*16*32*12=98304 Total: 13631488+98304=13729792 "five regular layers with the dimensions 256×256, 128×128, . . ., 16×16." "size of the receptive field to be 4 × 4 neurons". 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.
LCNP LabelMe— who created it?
It was published by University of Bonn, based in Germany, an organisation categorised as academia.
LCNP LabelMe— when was it released?
It was published in November 2009. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
LCNP LabelMe— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object recognition. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
LCNP LabelMe— how much compute was used to train it?
Training consumed around 3.3 × 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.
LCNP LabelMe— 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.