The AI Revolution: Unlocking the Power of Image Recognition in the Browser
The web has undergone a remarkable transformation, evolving from a document platform to a robust application platform. In recent years, artificial intelligence (AI) has experienced tremendous growth, and the web is now capable of delivering exceptional experiences that leverage AI advancements. With devices boasting impressive processing power and browsers that can fully utilize them, tech giants have invested heavily in making it easier for developers to integrate AI features into their web applications.
Three Major Image Classification Libraries
When it comes to image recognition, three libraries stand out from the rest: TensorFlow.js, ONNX.js, and WebDNN. Each of these libraries has its unique strengths and weaknesses, and understanding their differences is crucial for developers looking to harness the power of AI in their web applications.
TensorFlow.js: The Pioneer
Backed by Google, TensorFlow.js allows developers to create machine learning models in JavaScript and deploy them directly in the browser or Node.js. This library enables training and executing models in the browser, as well as retraining existing models using transfer learning and user data. The recent acquisition of Keras.js has brought significant improvements to TensorFlow, positioning it for even greater capabilities in the future.
ONNX.js: The Open Standard
The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models, supported by a community of partners including AWS, Facebook OpenSource, Microsoft, AMD, IBM, and Intel AI. ONNX.js leverages a combination of web workers and web assembly to achieve exceptional CPU performance, making it an attractive option for developers.
WebDNN: The Optimizer
WebDNN provides an efficient architecture for deep learning applications, including image recognition and language modeling, using convolutional and recurrent neural networks. This framework optimizes trained DNN models to compress model data and accelerate execution, utilizing novel JavaScript APIs such as WebAssembly and WebGPU to achieve zero-overhead execution.
Comparing Performance
To evaluate the performance of these libraries, we developed a React app using the Squeezenet model for image classification. Our results show that TensorFlow.js leads in CPU inference, while ONNX.js and WebDNN excel in WebAssembly and WebGL performance, respectively.
Backends Supported
Modern browsers offer four backends: WebMetal, WebGL, WebAssembly, and PlainJS. While all three libraries support CPU and WebGL backends, WebDNN takes the lead by allowing developers to leverage the WebMetal experimental feature. ONNX.js, on the other hand, smartly combines WASM and WebWorker to optimize CPU inferencing.
Browser Support and Popularity
Supporting all major browsers across different operating systems is a significant challenge when handling heavy computational tasks. Our analysis reveals that TensorFlow.js leads in adoption, while ONNX.js and WebDNN excel in performance, indicating a promising future for both.
The Future of AI in the Browser
TensorFlow.js, ONNX.js, and WebDNN each have their unique strengths, making them suitable foundations for AI-based web applications. As the web continues to evolve, we can expect to see even more innovative applications of AI in the browser.