Speech emotion recognition models for the Moody web application.
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Updated
Jul 5, 2021 - Jupyter Notebook
Speech emotion recognition models for the Moody web application.
We convert timm (Pytorch Image Models) models to .onnx and check their performance in browser using ONNX Runtime Web (ort-web). So you can find the suitable model for your JavaScript web-app according to your needs.
A simple Web AI model deployment tool using JavaScript based on OpenCV.js and ONNXRuntime
Convolutional AutoEncoders (MNIST) and their generative capabilities (kind of amazing)
Convert a PyTorch model and train it in JavaScript in your browser using ONNX Runtime Web
YOLOv5 model for table detection and text recognizing with Textract API
YOLOv5 Segmentation Right in The Browser Using onnxruntime-web
Web-based real-time object detection for YOLOv7 model.
A PoC to run Segment Anything Model (SAM) entirely in the browser without any backend
YOLOv8 Segmentation Right in The Browser Using onnxruntime-web
Client-side RetinaFace inference in vanilla JavaScript
An approach to make a more modular UI for ONNX Stable Diffusion, to ease future upgrades
Demos to run different AI models directly in browser
Ionic application for detection of tomato diseases on leaves
A game where players compete to draw differing prompts on a shared canvas, as scored by a computer vision model
ONNX runtime for Flutter.
Add a description, image, and links to the onnxruntime-web topic page so that developers can more easily learn about it.
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