OmniParser: Icon Description Model
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Microsoft Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 1 August 2024
- Authors
- Yadong Lu, Jianwei Yang, Yelong Shen, Ahmed Awadallah
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image captioning
- Base model
- BLIP-2 (Q-Former)
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
- tokens
- Epochs
- 1
"We finetune BLIP-2 model for 1 epoch on the generated dataset with constant learning rate of 1e−5 ,no weight decay and Adam optimizer. " "We we curate a dataset of 7k icon-description pairs using GPT-4o, and finetune a BLIP-v2 model [LLSH23] on this dataset" "we collected 7185 icon-description pairs for finetuning"
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- OmniParser for Pure Vision Based GUI Agent
- Last updated
- 28 November 2025
What the numbers mean
What this model is
OmniParser: Icon Description Model was published by Microsoft Research, in the country recorded as United States of America, during August 2024. The publishing organisation is categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of image captioning.
Its starting point was an existing base model, BLIP-2 (Q-Former). That is the usual way a specialised model is produced.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
OmniParser: Icon Description Model — common questions
OmniParser: Icon Description Model— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
OmniParser: Icon Description Model— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
OmniParser: Icon Description Model— 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.
OmniParser: Icon Description Model— who created it?
It was published by Microsoft Research, based in United States of America, an organisation categorised as industry.
OmniParser: Icon Description Model— when was it released?
It was published in August 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
OmniParser: Icon Description Model— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image captioning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
OmniParser: Icon Description Model— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.