wenke727

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

mmdetection

OpenMMLab Detection Toolbox and Benchmark

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

paper-reading

深度学习经典、新论文逐段精读

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

AirSim

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

carla

Open-source simulator for autonomous driving research.

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

segmentation_models.pytorch

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

PyTorch-Tutorial

Build your neural network easy and fast, 莫烦Python中文教学

faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

KeymouseGo

类似按键精灵的鼠标键盘录制和自动化操作 模拟点击和键入 | automate mouse clicks and keyboard input

HyperLPR

基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.

Deformable-DETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

LPRNet_Pytorch

Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.

RLcode

License_Plate_Detection_Pytorch

A two stage lightweight and high performance license plate recognition in MTCNN and LPRNet

pytorch-A3C

Simple A3C implementation with pytorch + multiprocessing

Lane-Segmentation-Solution-For-BaiduAI-Autonomous-Driving-Competition

Lane Segmentation Solution for Baidu AI PaddlePaddle Autonomous Driving Competition(1st Place)

rlds

CCTSDB

CSUST Chinese Traffic Sign Detection Benchmark

opendriveparser

OpenDRIVE Map parser

GTSRB

Convolutional Neural Network for German Traffic Sign Recognition Benchmark

heteta

HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival

gym-traffic

OpenAI Gym Environment for Traffic Control

map_matching

Algorithms to find the streets that a vehicle should have traveled to generate a given GPS track

Apollo--laneline-detection

python pytorch opencv resnet101_unet xception_65_deeplabv3plus

TrafficsSignDetection

this is a C++ program for traffic signs detection supported by OpenCV

Dive_into_Deep_Learning

动手学深度学习

AerialImageSegmentation

本次新人赛是Datawhale与天池联合发起的零基础入门系列赛事第七场 —— 零基础入门语义分割之地表建筑物识别挑战赛。 赛题以计算机视觉为背景，要求选手使用给定的航拍图像训练模型并完成地表建筑物识别任务。为更好的引导大家入门，我们为本赛题定制了学习方案和学习任务，具体包括语义分割的模型和具体的应用案例。在具体任务中我们将讲解具体工具和使用和完成任务的过程。 通过对本方案的完整学习，可以帮助掌握语义分割基本技能。同时我们也将提供专属的视频直播学习通道。 新人赛的目的主要是为了更好地带动处于初学者阶段的新同学们一起玩起来，因此，我们鼓励所有选手，基于赛题发表notebook分享，内容包含但不限于对赛题的理解、数据分析及可视化、算法模型的分析以及一些核心的思路等内容。