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yanghedada's Projects

protopnet icon protopnet

This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).

pruning icon pruning

CNN‘s model compression and pruning

pulse icon pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

pwc icon pwc

Papers with code. Sorted by stars. Updated weekly.

pysot icon pysot

SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

pytorch-unet icon pytorch-unet

Simple PyTorch implementations of U-Net/FullyConvNet (FCN) for image segmentation

raccoon_dataset icon raccoon_dataset

The dataset is used to train my own raccoon detector and I blogged about it on Medium

real-world-sr icon real-world-sr

[ICCVW 2019] PyTorch implementation of DSGAN and ESRGAN-FS from the paper "Frequency Separation for Real-World Super-Resolution". This code was the winning solution of the AIM challenge on Real-World Super-Resolution at ICCV 2019

self-driving-rc-car-payment icon self-driving-rc-car-payment

Self driving RC-car pays for car barrier on its own. This repository contains code for some autonomous car techniques applied to an RC-Car.

shiftcnn icon shiftcnn

A script to convert floating-point CNN models into generalized low-precision ShiftCNN representation

siamesex.pytorch icon siamesex.pytorch

A simplified PyTorch implementation of Siamese networks for tracking: SiamFC, SiamVGG, SiamDW, SiamRPN, SiamRPN-VGG.

sniper icon sniper

SNIPER is an efficient multi-scale object detection algorithm

text-classifier icon text-classifier

2018达观杯文本智能处理比赛,文本分类主题,最终排名 8/3462

tianchi-o2o icon tianchi-o2o

这个notebook是根据去年o2o比赛冠军的代码(https://github.com/wepe/O2O-Coupon-Usage-Forecast)整理注释的,使用notebook(安装了插件方便整理),加了一些自己的注释,调了bug,实测可以跑通。用xgboost应该在0.795左右。

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