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algorithm-base

专门为刚开始刷题的同学准备的算法基地，没有最细只有更细，立志用动画将晦涩难懂的算法说的通俗易懂！

alphapose

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

awesome-cs-books

经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

ddaloss-pytorch

The PyTorch implementation of Discriminant Distribution-Agnostic loss (DDA loss)

emotion-fan

ICIP 2019: Frame Attention Networks for Facial Expression Recognition in Videos

facial-expression-recognition.pytorch

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

facial_details_synthesis

Photo-Realistic Facial Details Synthesis from Single Image (ICCV2019 Oral)

fast-reid

SOTA Re-identification Methods and Toolbox

hello_world

human-pose-estimation.pytorch

The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(https://arxiv.org/abs/1804.06208)"

imgaug

Image augmentation for machine learning experiments.

learngit

leetcode-solutions

Solutions for LeetCode

micro-expression-with-deep-learning

Experimentation of deep learning on the subjects of micro-expression spotting and recognition.

mmaction2

OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

mspn

Multi-Stage Pose Network

pytorch-pretrained-vit

Vision Transformer (ViT) in PyTorch

self-cure-network

This is a novel and easy method for annotation uncertainties.

sixiping

Config files for my GitHub profile.

unipose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch