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This project uses yolov8 combined with bytetrack to achieve multi-target tracking
[ECCV2020] a toolbox of light-reid learning for faster inference, speed both feature extraction and retrieval stages up to >30x
SOTA Re-identification Methods and Toolbox
This repository contains the official implementation of GhostFaceNets, State-Of-The-Art lightweight face recognition models.
State-of-the-art 2D and 3D Face Analysis Project
A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.
Generative Agents: Interactive Simulacra of Human Behavior
An open source library for face detection in images. The face detection speed can reach 1000FPS.
The training program for libfacedetection for face detection and 5-landmark detection.
Lecture notes, projects and other materials for Course 'CS205 C/C++ Program Design' at Southern University of Science and Technology.
PyTorch implementation of EfficientNetV2 family
Winograd minimal convolution algorithm generator for convolutional neural networks.
An intelligent coding assistant plugin for Visual Studio Code, developed based on CodeShell
A library for efficient similarity search and clustering of dense vectors.
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./project/android/apps/MnnLlmA…
Visualizer for neural network, deep learning and machine learning models
Code for CRATE (Coding RAte reduction TransformEr).
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation
Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)
Empowering everyone to build reliable and efficient software.
The official PyTorch implementation of the paper: Xili Dai, Shengbang Tong, et al. "Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction.".