Hongyang Tang's starred repositories
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
OCHumanApi
API for the dataset proposed in "Pose2Seg: Detection Free Human Instance Segmentation" @ CVPR2019.
pytorch-doc-zh
Pytorch 中文文档
chinese-independent-developer
👩🏿💻👨🏾💻👩🏼💻👨🏽💻👩🏻💻**独立开发者项目列表 -- 分享大家都在做什么
tensorboardX
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
awesome-human-pose-estimation
Human Pose Estimation Related Publication
BigGAN-PyTorch
The author's officially unofficial PyTorch BigGAN implementation.
CVPR2024-Paper-Code-Interpretation
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集,极市团队整理
paper-tips-and-tricks
Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.
qiubaiying.github.io
BY Blog ->
conv_arithmetic
A technical report on convolution arithmetic in the context of deep learning
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)"
pose-hg-3d
Code repository for Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
pytorch-cpn
A PyTorch re-implementation of CPN (Cascaded Pyramid Network for Multi-Person Pose Estimation)
pytorch_geometric
Graph Neural Network Library for PyTorch
VideoPose3D
Efficient 3D human pose estimation in video using 2D keypoint trajectories
EpipolarPose
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)
Integral-Human-Pose-Regression-for-3D-Human-Pose-Estimation
PyTorch implementation of "Integral Human Pose Regression", ECCV 2018
PyTorchZeroToAll
Simple PyTorch Tutorials Zero to ALL!
Crack-Driving-Exam
A Jupyter-based interface for cracking the theoretical test of Chinese driving license exam
paper-gestalt
Deep Paper Gestalt
maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
Switchable-Normalization
Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv.org/abs/1806.10779