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LinggenWu's Projects

detectron icon detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

deye icon deye

Keep an Eye on Defects Inspection.

dl.eyesight icon dl.eyesight

Mainly use SSD, YOLO and other models to solve the target detection problem in image and video !

dsod icon dsod

DSOD: Learning Deeply Supervised Object Detectors from Scratch. In ICCV 2017.

easypr icon easypr

An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.

extended-kalman-filter icon extended-kalman-filter

Matlab and C++ code for implementation of the Extended Kalman Filter for estimating dynamic quantities for a single rigid body with distributed force/torque measurements and distributed gyroscopes and accelerometers measurements.It also include estimation of the orientation under the quaternion representation.

faceswap icon faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

hyperlpr icon hyperlpr

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

imageretrieval icon imageretrieval

Content Based Image Retrieval Techniques (e.g. knn, svm using MatLab GUI)

intro_ds icon intro_ds

Code to accompany Mastering Data Science from PT press

jumpcut icon jumpcut

Source code of SIGGRAPH Asia '2015 paper "JumpCut: Non-Successive Mask Transfer and Interpolation for Video Cutout" by Qingnan Fan et al.

kalman-and-bayesian-filters-in-python icon kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

kalman_filter icon kalman_filter

这是一个Kalman Filter的程序,内含状态量为标量和二维矢量的C程序实现

keras-yolo2 icon 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).

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