Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Implementation of Quickdraw - an online game developed by Google
| Date | Stars |
|---|---|
| 2026-07-24 | 1105 |
| 2026-07-25 | 1105 |
| 2026-07-28 | 1105 |
| 2026-07-30 | 1105 |
| 2026-08-10 | 1105 |
| 2026-09-01 | 1104 |
| 2026-09-04 | 1103 |
| 2026-09-07 | 1102 |
| 2026-09-20 | 1102 |
Today
— stars today
This week
— stars this week
This month
-3 stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center"> <h1 align="center">QuickDraw</h1> </p> [](https://github.com/uvipen/QuickDraw/stargazers) [](https://github.com/uvipen/QuickDraw/network) [](https://github.com/uvipen/QuickDraw/blob/master/LICENSE) ## Introduction Here is my python source code for QuickDraw - an online game developed by google. with my code, you could: * **Run an app which you could draw in front of a camera (If you use laptop, your webcam will be used by default)** * **Run an app which you could draw on a canvas** ## Camera app In order to use this app, you need a pen (or any object) with blue, red or green color. When the pen (object) appears in front of camera, it will be catched and highlighted by an yellow circle. When you are ready for drawing, you need to press **space** button. When you want to stop drawing, press **space** again Below is the demo by running the sript **camera_app.py**: <p align="center"> <img src="demo/quickdraw.gif" width=600><br/> <i>Camera app demo</i> </p> ## Drawing app The script and demo will be released soon ## Dataset The dataset used for training my model could be found at [Quick Draw dataset] https://console.cloud.google.com/storage/browser/quickdraw_dataset/sketchrnn. Here I only picked up 20 files for 20 categories ## Categories: The table below shows 20 categories my model used: | | | | | |-----------|:-----------:|:-----------:|:-----------:| | apple | book | bowtie | candle | | cloud | cup | door | envelope | |eyeglasses | guitar | hammer | hat | | ice cream | leaf | scissors | star | | t-shirt | pants | lightning | tree | ## Trained models You could find my trained model at **trained_models/whole_model_quickdraw** ## Training You need to download npz files corresponding to 20 classes my model used and store them in folder **data**. If you want to train your model with different list of categories, you only need to change the constant **CLASSES** at **src/config.py** and download necessary npz files. Then you could simply run **python3 train.py** ## Experiments: For each class, I take the first 10000 images, and then split them to training and test sets with ratio 8:2. The training/test loss/accuracy curves for the experiment are shown below: <img src="demo/loss_accuracy_curves.png" width="800"> ## Requirements * **python 3.6** * **cv2** * **pytorch** * **numpy**
Excerpt of 2,685 characters
Read on GitHub
Viet Nguyen · SPORTTOTAL.TV · Germany
11
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6604378a4f1476da, topic:deep-learning, topic:neural-network, topic:pytorch
matched fp:6604378a4f1476da, topic:computer-vision, topic:opencv, topic:image-classification