AI in browsers: Comparing TensorFlow, ONNX, and WebDNN for image classification

The evolution of the web from a document platform to a robust application platform has facilitated significant advancements in artificial intelligence, particularly in web development. Developers can now leverage powerful processing capabilities in modern browsers to integrate AI features directly into web applications using libraries like TensorFlow.js, ONNX.js, and WebDNN for image recognition tasks. TensorFlow.js, backed by Google, allows for machine learning model development in JavaScript, while ONNX.js, supported by major tech companies, excels in CPU performance through web workers and web assembly. WebDNN, on the other hand, focuses on optimizing deep neural networks for efficient execution using novel JavaScript APIs. Performance evaluations reveal that ONNX.js leads in WebGL inference speed, while TensorFlow.js enjoys the highest adoption rate among developers. Each library offers unique advantages, making them suitable for different AI-based web applications, and the ongoing improvements in hardware support suggest a promising future for AI capabilities in the browser environment.