赛题链接:https://www.kaggle.com/c/quickdraw-doodle-recognition 赛题描述: "Quick, Draw!" was released as an experimental game to educate the public in a playful way about how AI works. The game prompts users to draw an image depicting a certain category, such as ”banana,”“table,” ...
https://www.youtube.com/作者:Google Cloud Platform转载自:https://www.youtube.com/watch?v=8DEjphIfeYw【 人工智能 】 Quick Draw: the biggest doodle dataset(英文字幕)微博:宫帅USTC, 视频播放量 192、弹幕量 0、点赞数 4、投硬币枚数 0、收藏人数 13、转发人数
Quick on the Draw(1974–1979) TV Series|Comedy, Game-Show Edit pageAdd to list Track Cartoonists are asked to draw cartoons on a given subject. Seasons UnknownSee all Director Daphne Shadwell Writer Denis Gifford(deviser) See all filmmakers & crew (2) ...
ngxbac/Kaggle-QuickDraw Star11 Code Issues Pull requests Quick, Draw! Doodle Recognition Challenge (Rank 11/1316) kaggle-competitiondeeplearningquickdraw UpdatedDec 27, 2018 Python interlark/qdc-converter Star10 Code Issues Pull requests Garmin QDC (QuickDraw Contours) Converter to CSV tables and ...
Doodle Smith, an ML-powered web game that runs completely in your browser, thanks to Transformers.js! aineural-networkquickdrawmobilevitxenova-transformers UpdatedDec 15, 2023 JavaScript Implementation of Quickdraw - an online game developed by Google ...
Quick Draw is aninteractive, web-game experiencedesigned by Google's digital engineers. The site is meant to test, demonstrate, and raise awareness of Google's doodle recognition software, as well as the capabilities of their design team at large. The design of the site is meant toevoke a ...
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DoodleNet 是一个涂鸦分类器(CNN),对来自Quickdraw数据集的所有345个类别进行了训练。 Github项目地址: https://github.com/yining1023/doodleNet 这是我使用tensorflow.js和tensorflow进行的关于涂鸦分类器(一种卷积神经网络)的一系列实验。使用的数据来自Quickdraw数据集。
machine-learningcomputer-visiongame-developmentvector-graphicsquickdraw-datasetonnxruntime-web UpdatedJul 28, 2024 Python 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. ...
Once you open the webpage, wait until the page load the data, train the model, evaluate the model. It will download two files:myDoodleNet.jsonandmyDoodleNet.weights.bin. To test this model your self, you can load these two files back, and click on 'load model' button, then draw sth...