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The "Design2Code: How Far Are We From Automating Front-End Engineering" paper presents a benchmark for multimodal large language models (LLMs) aimed at automating front-end web development by translating webpage designs (screenshots) into code. This task evaluates the models' ability to recreate webpages that are visually and structurally similar to the original designs.
The "Design2Code: How Far Are We From Automating Front-End Engineering" paper presents a benchmark for multimodal large language models (LLMs) aimed at automating front-end web development by translating webpage designs (screenshots) into code. This task evaluates the models' ability to recreate webpages that are visually and structurally similar to the original designs.
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Key Points:
Key Points:
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* Introduces the Design2Code task and benchmark for converting webpage screenshots into code, aiming to automate front-end web development.
* Introduces the Design2Code task and benchmark for converting webpage screenshots into code, aiming to automate front-end web development.
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* Evaluates multimodal LLMs using comprehensive metrics for visual similarity and element matching.
* Evaluates multimodal LLMs using comprehensive metrics for visual similarity and element matching.
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* GPT-4V outperforms other models in terms of visual resemblance and content accuracy, with generated webpages often preferred over the original references.
* GPT-4V outperforms other models in terms of visual resemblance and content accuracy, with generated webpages often preferred over the original references.
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Paper: 
Paper: 
resource
https://huggingface.co/papers/2403.03163
paper
2403.03163
https://huggingface.co/papers/2403.03163
Design2Code: How Far Are We From Automating Front-End Engineering? (2403.03163)
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Project page: 
Project page: 
link
https://salt-nlp.github.io/Design2Code/
https://salt-nlp.github.io/Design2Code/
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Dataset: 
Dataset: 
resource
https://huggingface.co/datasets/SALT-NLP/Design2Code
dataset
SALT-NLP/Design2Code
https://huggingface.co/datasets/SALT-NLP/Design2Code
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Congrats to the authors for their work!
Congrats to the authors for their work!
The "Design2Code: How Far Are We From Automating Front-End Engineering" paper presents a benchmark for multimodal large language models (LLMs) aimed at automating front-end web development by translating webpage designs (screenshots) into code. This task evaluates the models' ability to recreate webpages that are visually and structurally similar to the original designs.

Key Points:
* Introduces the Design2Code task and benchmark for converting webpage screenshots into code, aiming to automate front-end web development.
* Evaluates multimodal LLMs using comprehensive metrics for visual similarity and element matching.
* GPT-4V outperforms other models in terms of visual resemblance and content accuracy, with generated webpages often preferred over the original references.

Paper: https://huggingface.co/papers/2403.03163
Project page: https://salt-nlp.github.io/Design2Code/
Dataset: https://huggingface.co/datasets/SALT-NLP/Design2Code

Congrats to the authors for their work!
❤️
osanseviero samusenps
👍
samusenps