ONNX Runtime Web Demo
ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS. It currently supports four examples for you to quickly experience the power of ONNX Runtime Web.
The demo is available here ONNX Runtime Web demo website.
NOTE: Currently, the supported platforms are Edge/Chrome/Firefox/Electron/Node.js (support for other platforms is coming soon).
Use Cases
The demo provides four scenarios based on four different ONNX pre-trained deep learning models.
1. MobileNet
MobileNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. MobileNet models are also very efficient in terms of speed and size and hence are ideal for embedded and mobile applications.
2. SqueezeNet
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.
3. FER+ Emotion Recognition
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.
4. Yolo
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.
5. MNIST
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!
Run ONNX Runtime Web Demo
Install Dependencies
npm install
Serve the demo
Serve the demo in localhost
npm run serve
This will start a dev server and run ONNX Runtime Web demo on your localhost.
Deploy the demo
npm run build
This will pack the source files into /docs folder and be ready for deployment.
- Electron support
ONNX Runtime Web 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 /ONNXRuntimeWeb-demo-win32-x64 folder. Run /ONNXRuntimeWeb-demo-win32-x64/ONNXRuntimeWeb-demo.exe to enjoy Electron desktop app.
Credits
This demo is adapted from keras.js demo. Modifications have been made to the UI and the backend uses ONNX Runtime Web.
GitHub
microsoft
