wenke727's starred repositories
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.
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
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.
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)
opendriveparser
OpenDRIVE Map parser
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。