Implementation of Quickdraw - an online game developed by Google

Implementation of QuickDraw - an online game developed by Google, combined with AirGesture - a simple gesture recognition application

Model and Android app for sketch recognition using Google's quickdraw dataset

A game where players compete to draw differing prompts on a shared canvas, as scored by a computer vision model

An easy to read and Object Oriented implementation of a simple Neural Network using back-propagation and hidden layers, applied on a basic image classification problem.

Web app to detect user hand-drawn sketches on a canvas. Using google's quickdraw dataset. App built using python with flask and keras API.

PyTorch implementation of the conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for the Google's "Quick, Draw!" dataset.

Keras light-weight model for sketch images classification using Quick!Draw dataset

CDCGAN Generator and ResNet34 Classifier for QuickDraw! dataset from Google

Implementation of a Generative Adversarial Network (GAN) to create synthetic images from Google’s Quick Draw dataset. This project explores adversarial training, dataset preprocessing, and critical evaluation of generative models.

Yet another disentangled VAE ... but for quick drawing doodles

Conditional GAN for the quickdraw dataset

This project tries to create new doodles using GAN. This project uses google's quick draw data set

ndjsonTosvg to convert Google Quickdraw data set ndjson format to svg i mages

Neural network built from scratch for classifying Quick, Draw! doodles. Work in progress combining CNN and DNN, trained on a subset of figures.

Quickdraw_grid generates a grid of vector drawings from Google's "Quick, Draw!" database, based on user's input - selected category, number of rows and columns.

A Python based TwitterBot implementation that is multi-featured with tweeting, chatting, and drawing.

Convolutional Neural Network trained to classify hand-drawn images