Parker-Lyu

TensorFLow-Learning

B站上炼数成金的公开课笔记

rscup2019_classifier

rscup2019，分类赛道

Uda-SelfCar

Udacity的无人驾驶学位

DeepL_Andrew_And_Uda

Andrew以及Udacity的深度学习笔记及相关代码

Coursera-ML-AndrewNg-Notes

吴恩达老师的机器学习课程个人笔记

deeplearning.ai

deeplearning.ai , By Andrew Ng, All slide and notebook + code and some material.

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

BiSeNet

mIOU=78.4 on cityscapes. My implementation of paper: BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

cn-deep-learning

https://cn.udacity.com/course/deep-learning-nanodegree-foundation--nd101/

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

insightface

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

jd-seckill-maotai

京东茅台抢购最新优化版本，京东秒杀，添加误差时间调整，优化了茅台抢购进程队列

LearnGithub

to learn how to user github

mean-teacher

A state-of-the-art semi-supervised method for image recognition

mixup-cifar10

mixup: Beyond Empirical Risk Minimization

ML_BY_Andrew

吴恩达机器学习作业及相关代码python实现

pytorch-CartoonGAN

Pytorch implementation of CartoonGAN (CVPR 2018)

pytorch-distributed

A quickstart and benchmark for pytorch distributed training.

PyTorch-YOLOv3

Minimal PyTorch implementation of YOLOv3

pytorch-YOLOv4

PyTorch ,ONNX and TensorRT implementation of YOLOv4

README

README文件语法解读，即Github Flavored Markdown语法介绍

recipes

SOLO

SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

STCN

[NeurIPS 2021] Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

Tensorrt_Mask2Former

TorchSeg

Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.

Uda-ComputerVersion

优达学城，计算机视觉，课内练习，及项目

XMem

[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

zhihu

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.