@vladmandic/human
Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition
Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition
TensorFlow.js platform implementation for React Native
FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS
Simplifies integration with Teachable Machine models from Google
Create tensors directly from CSV files. Supports operations like standardisation so you can dive right into the fun parts of ML.
A Node-RED node that uses tensorflowjs for object detection.
Detect multiple faces within a group photo. Powered by PoseNet model from tfjs-models (TensorflowJS).
![image](https://user-images.githubusercontent.com/48346627/104487132-0b101180-5610-11eb-8182-b1be3470c9c9.png)
This is webworker module for [Face landmarks detection](https://github.com/tensorflow/tfjs-models/tree/master/face-landmarks-detection).
This is webworker module for [Posenet](https://github.com/tensorflow/tfjs-models/tree/master/posenet).
Identify objects in an image, additionally assigning each pixel of the image to a particular object.
Simplifies integration with Teachable Machine models from Google
A package for creating image-based machine learning models
Deep learning magic.. with the convenience of cat!
A machine learning engine for quickly training image classification models in your browser
Simple multi-layer perceptron implemented in a functional style with typescript and node
Face detector primitive derived from tensorflowjs
A machine learning engine for quickly training image classification models in your browser
Find Whatsoever in image with the convenience of Machine learning at CLI!
Inline TensorFlowJS models in your app bundle. Simply import weight manifest .bin file and get associated model.json as a blob URL