Unlocking the conversion of Web Screenshots into HTML Code with the
  WebSight Dataset
Using vision-language models (VLMs) in web development presents a promising
strategy to increase efficiency and unblock no-code solutions: by providing a
screenshot or a sketch of a UI, a VLM could generate the code to reproduce it,
for instance in a language like HTML. Despite the advancements in VLMs for
various tasks, the specific challenge of converting a screenshot into a
corresponding HTML has been minimally explored. We posit that this is mainly
due to the absence of a suitable, high-quality dataset. This work introduces
WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and
their corresponding screenshots. We fine-tune a foundational VLM on our dataset
and show proficiency in converting webpage screenshots to functional HTML code.
To accelerate the research in this area, we open-source WebSight.
65f3a30e2b4e85e2e8898b2d
Hugo Laurençon
admin_assigned
2024-03-15T08:42:49.912Z
6177322d37f32ecb1e2d4cdf
https://cdn-avatars.huggingface.co/v1/production/uploads/1635201569275-noauth.jpeg
Hugo Laurençon
user
HugoLaurencon
65f3a30e2b4e85e2e8898b2e
Léo Tronchon
admin_assigned
2024-03-15T08:43:01.647Z
6244866a456803e9500d0f6a
https://cdn-avatars.huggingface.co/v1/production/uploads/1652185658647-6244866a456803e9500d0f6a.jpeg
Leo Tronchon
user
Leyo
65f3a30e2b4e85e2e8898b2f
Victor Sanh
admin_assigned
2024-03-15T08:43:09.692Z
5ecea265968f6028e0559fa5
https://cdn-avatars.huggingface.co/v1/production/uploads/1619623771844-5ecea265968f6028e0559fa5.jpeg
Victor Sanh
user
VictorSanh
Unlocking the conversion of Web Screenshots into HTML Code with the
  WebSight Dataset
Using vision-language models (VLMs) in web development presents a promising
strategy to increase efficiency and unblock no-code solutions: by providing a
screenshot or a sketch of a UI, a VLM could generate the code to reproduce it,
for instance in a language like HTML. Despite the advancements in VLMs for
various tasks, the specific challenge of converting a screenshot into a
corresponding HTML has been minimally explored. We posit that this is mainly
due to the absence of a suitable, high-quality dataset. This work introduces
WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and
their corresponding screenshots. We fine-tune a foundational VLM on our dataset
and show proficiency in converting webpage screenshots to functional HTML code.
To accelerate the research in this area, we open-source WebSight.
A synthetic dataset of HTML code and screenshots is introduced to enhance the ability of vision-language models to convert screenshots into HTML code.
vision-language models VLMs WebSight synthetic dataset HTML codes screenshots fine-tune