feng xt's starred repositories
awesome-3D-gaussian-splatting
Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.
HuggingFace-Download-Accelerator
利用HuggingFace的官方下载工具从镜像网站进行高速下载。
sanitizers
AddressSanitizer, ThreadSanitizer, MemorySanitizer
HowToLiveLonger
程序员延寿指南 | A programmer's guide to live longer
caffe-onnx
caffe model convert to onnx model
gperftools
Main gperftools repository
awesome-reid-dataset
Collection of public available person re-identification datasets
awesome_video_person_reid
papers collection and understanding for video person re-identification
Background-Matting
Background Matting: The World is Your Green Screen
global-matting
An implementation of global matting algorithm for OpenCV.
2018--ZJUAI--PyramidBoxDetector
2018 云从人头技术冠军分享方案
multi-object-tracking-paper-list
Paper list and source code for multi-object-tracking
deep-head-pose
:fire::fire: Deep Learning Head Pose Estimation using PyTorch.
pose-residual-network-pytorch
Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network' paper https://arxiv.org/abs/1807.04067
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)"
Multitarget-tracker
Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.
Paddle-Lite
PaddlePaddle High Performance Deep Learning Inference Engine for Mobile and Edge (飞桨高性能深度学习端侧推理引擎）
deep-person-reid
Torchreid: Deep learning person re-identification in PyTorch.
person-reid-benchmark
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
person-re-ranking
Person Re-ranking (CVPR 2017)
Person_reID_baseline_pytorch
:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
keras-yolo2
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).