cogagent-chat-hf

Maintainer: THUDM

Total Score

51

Last updated 5/27/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The cogagent-chat-hf is an open-source visual language model improved based on CogVLM. Developed by THUDM, this model demonstrates strong performance in image understanding and GUI agent capabilities.

CogAgent-18B, the version of this model, has 11 billion visual and 7 billion language parameters. It achieves state-of-the-art generalist performance on 9 cross-modal benchmarks, including VQAv2, MM-Vet, POPE, ST-VQA, OK-VQA, TextVQA, ChartQA, InfoVQA, and DocVQA. Additionally, CogAgent-18B significantly surpasses existing models on GUI operation datasets like AITW and Mind2Web.

Compared to the original CogVLM model, CogAgent supports higher resolution visual input and dialogue question-answering, possesses the capabilities of a visual Agent, and has enhanced GUI-related and OCR-related task capabilities.

Model inputs and outputs

Inputs

  • Images: CogAgent-18B supports ultra-high-resolution image inputs of 1120x1120 pixels.
  • Text: The model can handle text inputs for tasks like visual multi-round dialogue, visual grounding, and GUI-related question-answering.

Outputs

  • Visual Agent Actions: CogAgent-18B can return a plan, next action, and specific operations with coordinates for any given task on a GUI screenshot.
  • Text Responses: The model can provide text-based answers to questions about images, GUIs, and other visual inputs.

Capabilities

CogAgent-18B demonstrates strong performance in various cross-modal tasks, particularly in image understanding and GUI agent capabilities. It can handle tasks like visual multi-round dialogue, visual grounding, and GUI-related question-answering with high accuracy.

What can I use it for?

The cogagent-chat-hf model can be useful for a variety of applications that involve understanding and interacting with visual content, such as:

  • GUI Automation: The model's ability to recognize and interact with GUI elements can be leveraged to automate various GUI-based tasks, such as web scraping, app testing, and workflow automation.
  • Visual Dialogue Systems: The model's capabilities in visual multi-round dialogue can be used to build conversational AI assistants that can understand and discuss images and other visual content.
  • Image Understanding: The model's strong performance on benchmarks like VQAv2 and TextVQA makes it suitable for developing applications that require advanced image understanding, such as visual question-answering or image captioning.

Things to try

One interesting aspect of the cogagent-chat-hf model is its ability to handle ultra-high-resolution image inputs of up to 1120x1120 pixels. This allows the model to process detailed visual information, which could be useful for applications that require analyzing complex visual scenes or high-quality images.

Another notable feature is the model's capability as a visual agent, which allows it to return specific actions and operations for given tasks on GUI screenshots. This could be particularly useful for building applications that automate or assist with GUI-based workflows, such as web development, software testing, or data extraction from online platforms.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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