iCCCP
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
- Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 13 June 2010
- Authors
- Long Zhu; Yuanhao Chen; Alan Yuille; William Freeman
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
- 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.
- Training data
- 10,000 tokens
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
- 1.1 × 10¹⁵ FLOP
- How it was established
- Hardware
“All experiments are performed on a standard computer with a 3Ghz CPU” “It takes 25 hours (about 25 iCCCP iterations) to train an object class with two mixture templates.” Individual models for some of the classes. Assuming a Core 2 with 8 FLOP/cycle was used with a utilization of 0.5: 3000000000*8*25*60*60*0.5=1080000000000000
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 1
- Wall-clock time
- 25 hours
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
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,SOTA improvement
- Record confidence
- Confident
"We perform object detection using our learnt models and obtain performance comparable with state-of-the-art methods when evaluated on challenging public PASCAL datasets. "
Sources
Where this record came from and when it was last checked.
- Reference
- Latent hierarchical structural learning for object detection
- Last updated
- 28 November 2025
What the numbers mean
About this model
iCCCP was published by Massachusetts Institute of Technology (MIT), in the country recorded as United States of America, during June 2010. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of object detection.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 1.1 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 10,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: historical significance,SOTA improvement.
Answers
iCCCP — common questions
iCCCP— who created it?
It was published by Massachusetts Institute of Technology (MIT), based in United States of America, an organisation categorised as academia.
iCCCP— when was it released?
It was published in June 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
iCCCP— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
iCCCP— how much compute was used to train it?
Training consumed around 1.1 × 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.
iCCCP— 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.
iCCCP— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
iCCCP— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
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.