Popular GitHub Repositories for Brain Age Estimation

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

State-of-the-art 2D and 3D Face Analysis Project

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

? GPU accelerated Neural networks in JavaScript for Browsers and Node.js

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

OpenMMLab Pose Estimation Toolbox and Benchmark.

Google Brain AutoML

Gluon CV Toolkit

Effortless data labeling with AI support from Segment Anything and other awesome models.

Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

ECCV2022 - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Relax! Flux is the ML library that doesn't make you tensor

Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including OpenAI Agents SDK, CrewAI, Langchain, Autogen, AG2, and CamelAI

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"