Care sunt principalele funcționalități ale ashnkumar/sketch-code?

Principalele funcționalități ale ashnkumar/sketch-code sunt: Sketch-to-HTML Converters, Mockup-to-HTML Converters, Keras Model Implementations, Image-to-Code Networks, Visual Element Recognition Training, Wireframe Parsers, Deep Learning Design Tools, Hand-Drawn Code Conversion.

Care sunt câteva alternative open-source pentru ashnkumar/sketch-code?

Alternativele open-source pentru ashnkumar/sketch-code includ: sawyerhood/draw-a-ui — draw-a-ui is an AI vision UI generator and sketch-to-code tool that transforms hand-drawn sketches and digital… fchollet/deep-learning-models — This project is a collection of deep learning tools for image classification and audio tagging, providing a repository… heyform/heyform — Heyform is an open-source form builder and self-hosted data collection platform. It provides a no-code designer for… fastai/course22 — This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It… matterport/mask_rcnn — This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision…

Sketch Code

Sketch-code este un instrument de prototipare web bazat pe deep learning și un convertor de imagine în HTML, conceput pentru a transforma schițele desenate manual ale site-urilor web în cod HTML structural. Utilizează un parser de wireframe-uri cu rețele neuronale pentru a recunoaște elementele vizuale din schițe și a le mapa la reprezentările corespunzătoare de layout web.

Sistemul folosește o arhitectură bazată pe Keras pentru antrenarea și evaluarea modelelor de recunoaștere, folosind seturi de date de wireframe-uri desenate manual și HTML de referință. Acesta decuplează layout-ul structural de designul vizual, permițând injectarea de straturi CSS personalizate în timpul procesului de compilare.

Proiectul acoperă prototiparea frontend automatizată și generarea de layout-uri UI, incluzând maparea supervizată imagine-la-cod și evaluarea acurateței predicției pentru a măsura rezultatul generat față de referințele de tip ground-truth.

Features

Sketch-to-HTML Converters - Transforms hand-drawn website sketches into structured HTML code using a deep learning model for rapid prototyping.
Mockup-to-HTML Converters - Provides a deep learning model that translates visual mockups of website layouts into functional HTML code.
Keras Model Implementations - Uses the Keras API to define and optimize the neural network layers responsible for visual-to-code translation.
Image-to-Code Networks - Implements a neural network specialized in mapping visual pixels from hand-drawn sketches to structural markup language.
Visual Element Recognition Training - A capability in the project to train a network to identify wireframe elements using custom datasets and pretrained weights.
Wireframe Parsers - Utilizes a neural network designed to recognize hand-drawn visual elements and map them to web layout representations.
Deep Learning Design Tools - Acts as a deep learning-powered prototyping tool that converts sketches into functional HTML.
Hand-Drawn Code Conversion - Turns hand-drawn sketches of website layouts into functional HTML code using deep learning.
Frontend Prototyping Tools - Provides automated tools to convert visual mockups and sketches into functional frontend code to accelerate prototyping.
Hybrid CNN-RNN Architectures - Implements a hybrid CNN-RNN architecture to translate visual wireframe features into structural HTML sequences.
Vision Model Training - Implements a framework for training vision models to recognize hand-drawn wireframes and map them to HTML.
Image-To-Code Model Training - Trains a neural network to map hand-drawn visual elements to HTML representations using specialized datasets.
CSS Styling - Supports applying custom visual styles through a decoupled CSS injection layer during the compilation process.
Generative UI Layouts - Generates structural UI layouts from image inputs, allowing for a separate layer of visual design.
CSS Style Injections - Provides a mechanism to inject custom CSS layers post-generation to decouple structural layout from visual styling.

Istoric stele

ashnkumarsketch-code

Name: ashnkumar/sketch-code
Author: ashnkumar

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5,143 stele681 fork-uriPython3 vizualizări

Sketch Code

Features

Sketch-to-HTML Converters - Transforms hand-drawn website sketches into structured HTML code using a deep learning model for rapid prototyping.
Mockup-to-HTML Converters - Provides a deep learning model that translates visual mockups of website layouts into functional HTML code.
Keras Model Implementations - Uses the Keras API to define and optimize the neural network layers responsible for visual-to-code translation.
Image-to-Code Networks - Implements a neural network specialized in mapping visual pixels from hand-drawn sketches to structural markup language.
Visual Element Recognition Training - A capability in the project to train a network to identify wireframe elements using custom datasets and pretrained weights.
Wireframe Parsers - Utilizes a neural network designed to recognize hand-drawn visual elements and map them to web layout representations.
Deep Learning Design Tools - Acts as a deep learning-powered prototyping tool that converts sketches into functional HTML.
Hand-Drawn Code Conversion - Turns hand-drawn sketches of website layouts into functional HTML code using deep learning.
Frontend Prototyping Tools - Provides automated tools to convert visual mockups and sketches into functional frontend code to accelerate prototyping.
Hybrid CNN-RNN Architectures - Implements a hybrid CNN-RNN architecture to translate visual wireframe features into structural HTML sequences.
Vision Model Training - Implements a framework for training vision models to recognize hand-drawn wireframes and map them to HTML.
Image-To-Code Model Training - Trains a neural network to map hand-drawn visual elements to HTML representations using specialized datasets.
CSS Styling - Supports applying custom visual styles through a decoupled CSS injection layer during the compilation process.
Generative UI Layouts - Generates structural UI layouts from image inputs, allowing for a separate layer of visual design.
CSS Style Injections - Provides a mechanism to inject custom CSS layers post-generation to decouple structural layout from visual styling.

Alternative open-source pentru Sketch Code

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu Sketch Code.

sawyerhood/draw-a-ui
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fastai/course22
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This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
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Vezi toate cele 30 alternative pentru Sketch Code

Întrebări frecvente

Ce face ashnkumar/sketch-code?

Sketch Code

Features

Istoric stele

Sketch Code

Features

Alternative open-source pentru Sketch Code

SawyerHood/draw-a-ui

fchollet/deep-learning-models

heyform/heyform

fastai/course22

Întrebări frecvente

Istoric stele

Alternative open-source pentru Sketch Code

SawyerHood/draw-a-ui

fchollet/deep-learning-models

heyform/heyform

fastai/course22

Întrebări frecvente