ONNX.js demo is an interactive demo portal showing real use cases running ONNX.js runtime in VueJS. It currently supports four examples for you to quickly experience the power of ONNX.js runtime.
The demo is available here ONNX.js demo website.
NOTE: Currently, the supported platforms are Edge/Chrome/Firefox/Electron/Node.js (support for other platforms is coming soon).
The demo provides four scenarios based on four different ONNX pre-trained deep learning models.
SqueezeNet is a light-weight convolutional network for image classification. In the demo, you can select or upload an image and see which category it's from in miliseconds.
ResNet-50 is a highly-accurate deep convolutional network for image classification. It is trained on 1000 pre-defined classes. Similar to the SqueezeNet demo, you can select or upload an image and see which category it's from.
Emotion Ferplus is a deep convolutional neural network for emotion recognition in faces. In the demo, you can choose to either select an image with any human face or to start a webcam and see what emotion it's showing.
Yolo is a real-time neural network for object detection. It can detect 20 different objects such as person, potted plant and chair. In the demo, you can choose to either select an image or start a webcam to see what objects are in it.
MNIST is a convolutional neural network that predicts handwritten digits. In the demo, you can draw any number on the canvas and the model will tell you what number it is!
npm install
Serve the demo in localhost
npm run serve
This will start a dev server and run ONNX.js demo on your localhost.
npm run build
This will pack the source files into /docs folder and be ready for deployment.
- Electron support
Onnx.js demo can also serve as a Windows desktop app using Electron.
First create a developer build of the app by running
npm run build -- --mode developer
Then run
npm run electron-packager
This will create a new /ONNXjs-demo-win32-x64 folder. Run /ONNXjs-demo-win32-x64/ONNXjs-demo.exe to enjoy Electron desktop app.
This demo is adapted from keras.js demo. Modifications have been made to the UI and the backend uses ONNX.js.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.