VLM_WebSight_finetuned
Maintainer: HuggingFaceM4
157
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The VLM_WebSight_finetuned model is a vision-language model developed by HuggingFaceM4. It has been fine-tuned on the Websight dataset to convert screenshots of website components into HTML/CSS code. This model is based on a very early checkpoint of an upcoming vision-language foundation model, and is intended as an initial step towards improving models that can generate actual code from website screenshots.
Similar models include CogVLM, a powerful open-source visual language model that excels at various cross-modal tasks, and BLIP, a model that can perform both vision-language understanding and generation tasks.
Model inputs and outputs
Inputs
- Screenshots of website components: The model takes in screenshot images of website elements as input.
Outputs
- HTML/CSS code: The model outputs HTML and CSS code that represents the input website screenshot.
Capabilities
The VLM_WebSight_finetuned model can convert visual representations of website components into their corresponding HTML and CSS code. This allows users to quickly generate working code from website screenshots, which could be useful for tasks like web development, UI prototyping, and automated code generation.
What can I use it for?
The VLM_WebSight_finetuned model could be used in a variety of web development and design workflows. For example, you could use it to quickly generate HTML/CSS for mockups or initial website designs, saving time and effort compared to manually coding the layouts. It could also be integrated into tools for automating the conversion of design files into production-ready code.
Things to try
One interesting thing to try with this model is to see how it handles different types of website components, from simple layouts to more complex UI elements. You could experiment with providing screenshots of various website features and evaluating the quality and accuracy of the generated HTML/CSS code. This could help identify areas where the model performs well, as well as opportunities for further improvements.
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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