From 971c611434fc341f7004cea72dd10e3f42875b7e Mon Sep 17 00:00:00 2001 From: Honry Date: Fri, 16 Aug 2024 07:16:20 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20webmachi?= =?UTF-8?q?nelearning/webnn-samples@4f3103be036ae112ee82c2a27f6c5fc9376e5c?= =?UTF-8?q?e9=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index a684a126..43bc62cd 100644 --- a/README.md +++ b/README.md @@ -7,10 +7,12 @@ This repository contains a collection of samples and examples demonstrating Web ## Repository Structure This repository hosts a wide range of samples and examples that showcase different use cases and functionalities of WebNN. Here's an overview of the directory structure: +* [Code Editor](/code): This is a Code Editor used for evaluating, reviewing and modifying WebNN sample codes interactively in web browser. * [Face recognition](/face_recognition): This directory contains examples of SSD MobileNet V2 Face and Face Landmark (SimpleCNN) model implementation. * [Facial landmark detection](/facial_landmark_detection): This directory contains examples of SSD MobileNet V2 Face and Face Landmark (SimpleCNN) model implementation. * [Image classification](/image_classification): This directory contains examples demonstrating image classification using pre-trained models with WebNN. * [LeNet](/lenet): This example showcases the LeNet-based handwritten digits classification by WebNN API. +* [NNotepad](/nnotepad): This is a browser-based playground for experimenting with WebNN expressions without boilerplate code. * [NSNet2](/nsnet2): This example shows how to implement the NSNet2 baseline implementation of a deep learning-based noise suppression model. * [Object detection](/object_detection): Samples showcasing object detection tasks using WebNN with pre-trained models. * [RNNoise](/rnnoise): This example shows the RNNoise baseline implementation of a deep learning-based noise suppression model. @@ -63,6 +65,7 @@ To learn more about Web Neural Network API (WebNN) and its capabilities, check o ### WebNN API Samples * [WebNN code editor](https://webmachinelearning.github.io/webnn-samples/code/) +* [NNotepad](https://webmachinelearning.github.io/webnn-samples/nnotepad/) * [Handwritten digits classification](https://webmachinelearning.github.io/webnn-samples/lenet/) * Noise suppression: * [NSNet2](https://webmachinelearning.github.io/webnn-samples/nsnet2/)