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The development of artificial intelligence technology has led to industrial upgrading in the fields of computer vision(CV) and natural language processing(NLP). In addition, the deployment of AI models in browsers to achieve front-end intelligence has already provided good basic conditions with the steady increase in computing power on PCs and mobile devices, iterative updates of model compression technologies, and the continuous emergence of various innovative needs. In response to the difficulty of deploying AI deep learning models on the front-end, Baidu has open-sourced the Paddle.js front-end deep learning model deployment framework, which can easily deploy deep learning models into front-end projects.
Paddle.js is a web sub-project of Baidu PaddlePaddle, an open source deep learning framework running in the browser. Paddle.js can load the deep learning model trained by PaddlePaddle, and convert it into a browser-friendly model through the model conversion tool paddlejs-converter of Paddle.js, which is easy to use for online reasoning and prediction. Paddle.js supports running in browsers of WebGL/WebGPU/WebAssembly, and can also run in the environment of Baidu applet and WeChat applet.
Finally, we can launch AI functions in front-end application scenarios such as browsers and mini-program using Paddle.js, including but not limited to AI capabilities such as object detection, image segmentation, OCR, and item classification.
Refer to this document for steps to run computer vision demo in the browser.
| demo | web demo directory | visualization |
|---|---|---|
| object detection | ScrewDetection、FaceDetection |  |
| human segmentation | HumanSeg |  |
| classification | GestureRecognition、ItemIdentification |  |
| OCR | TextDetection、TextRecognition |  |
Run the official demo reference in the WeChat mini-program document
| Name | Directory |
|---|---|
| OCR Text Detection | ocrdetecXcx |
| OCR Text Recognition | ocrXcx |
| object detection | coming soon |
| Image segmentation | coming soon |
| Item Category | coming soon |
Thanks to Paddle Paddle Developer Expert (PPDE) Chen Qianhe (github: chenqianhe) for the Web demo, mini-program.