red-hai Goto Github PK

-

advanced_lane_detection

Advanced lane detection using computer vision

atlas_200dk_yolov3_tf_cpp

atlas_200dk_yolov3_v5_python

atlas_lanedetection

Lane Detection using opencv on Atlas200dk

auto_pid_controller

Control heater with STM32F103C8T6

autopid

Auto tuning PID controller in C

autowarearchitectureproposal

This is the source code of the feasibility study for Autoware architecture proposal.

awesome-public-datasets

A topic-centric list of HQ open datasets.

awesome_time_series_in_python

This curated list contains python packages for time series analysis

captainblackboard

船长关于机器学习、计算机视觉和工程技术的总结和分享

chedaojiance

code

daily-paper-computer-vision

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

darknet2caffe

Convert darknet weights to caffemodel

deeplabv3-for-custom-data-colab

Training your own dataset using Deeplab v3 in Google Colab

deeplearningandpaddletutorial-v2

depix

Recovers passwords from pixelized screenshots

dress

好耶 是女装

elliptic-curve-cryptography

Implement ECC and ECDSA in Verilog

embeddedfacerecognition

etna

ETNA – Time-Series Library

fast

开源SDN交换机项目-FAST 项目背景 现有SDN交换机开源项目（如OVS）主要基于软件编写，虽然其分组转发查表等功能在内核中实现，但分组转发性能仍然是主要问题。将软件SDN交换机的分组转发模块卸载到FPGA中实现，不仅能提高交换性能，而且利用FPGA可重构特性可以扩展SDN交换机的功能。 基于NetMagic平台已经有OFS-08和OFS-pro两款支持openflow1.3的SDN交换机实现，在当前的教学科研中得到广泛应用。但这两款平台中，FPGA主要实现CPU的端口扩展功能，openflow转发平面的功能，如协议分析，多元组查表等并没有卸载到FPGA中进行加速，因此在性能上难以进一步提升。 FAST模型 FAST（FPGA Accel

g-darknet

darknet with GIoU

handson-ml

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

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.

hls4ml

Machine learning on FPGAs using HLS

iiot

学习工业互联网4.0

improving_segmentation_with_selfsupervised_depth

An implementation of our work "Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation"