5 个仓库
A pre-trained neural network that categorizes images into NSFW and safe content classes using TensorFlow.js.
Distinct from Image Classification Models: Distinct from Image Classification Models: specifically built with TensorFlow.js for browser and Node.js deployment, targeting NSFW content.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · TensorFlow.js Models. Refine with filters or upvote what's useful.
TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py
Provides pre-trained models to identify and filter adult or inappropriate visual content on the client side.
NSFW detection on the client-side via TensorFlow.js
A pre-trained neural network that categorizes images into NSFW and safe content classes using TensorFlow.js.
Kalidokit is a web-based motion capture tool that transforms real-time webcam video into 3D character animation data. It functions as a blendshape and kinematics calculator, converting facial, hand, and body tracking data from Mediapipe and TensorFlow.js into blendshape weights and euler rotations for driving digital puppets and avatars. The tool solves face landmarks to derive head rotation, eye blinks, mouth shapes, and brow values for rigging, while hand landmarks are converted into finger joint rotations and body keypoints into per-joint euler rotations for full-body animation. It include
Processes real-time webcam landmark data from TensorFlow.js models into rig-ready animation parameters for avatars.
Real-Time-Person-Removal 是一个基于 Web 的计算机视觉应用,旨在识别并从实时视频流中移除人物。该工具使用 TensorFlow.js,作为一个实时背景减除系统,通过分析场景构成将静态背景与移动人物隔离开来。 该项目通过直接在客户端处理摄像头视频流,实现了基于浏览器的计算机视觉。它利用机器学习来区分动态场景元素和背景,从而实现视觉区域内人物的实时移除。
Uses TensorFlow.js to identify and remove people from video streams in real time.
This project is a JavaScript library designed for real-time face detection directly within a web browser. It functions as a machine learning model wrapper that enables developers to identify and track human faces in live video streams without the need for backend server processing. The library utilizes browser-native media access to stream raw camera data into application memory, where it performs pixel-level analysis. By leveraging a tensor-based inference engine and web-assembly acceleration, the tool executes complex neural network calculations locally to achieve high-performance computer
Executes heavy mathematical operations for neural network calculations using low-level binary instructions within the browser.