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Hou Lin Jie's Projects

pcl icon pcl

Point Cloud Library (PCL)

planematch icon planematch

[ECCV'18 Oral] PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction

planenet icon planenet

PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image

polarmask icon polarmask

Code for 'PolarMask: Single Shot Instance Segmentation with Polar Representation'

preciseroipooling icon preciseroipooling

Precise RoI Pooling with coordinate gradient support, proposed in the paper "Acquisition of Localization Confidence for Accurate Object Detection" (https://arxiv.org/abs/1807.11590).

py-faster-rcnn icon py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

pycls icon pycls

Codebase for Image Classification Research, written in PyTorch.

python icon python

All Algorithms implemented in Python

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-hardnet icon pytorch-hardnet

35% faster than ResNet: Harmonic DenseNet, A low memory traffic network

pytorchnethub icon pytorchnethub

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r-fcn icon r-fcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

racingrobot icon racingrobot

Autonomous Racing Robot With an Arduino, a Raspberry Pi and a Pi Camera

rcnn icon rcnn

R-CNN: Regions with Convolutional Neural Network Features

repulsion_loss_ssd icon repulsion_loss_ssd

Repulsion Loss: Detecting Pedestrians in a Crowd. https://arxiv.org/abs/1711.07752

resnext icon resnext

Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks

rfbnet icon rfbnet

Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

roomnet icon roomnet

A lightweight ConvNet (~800 KB) to classify pictures of different rooms of a house/apartment with 88.9 % accuracy

rsgunet_image_enhance icon rsgunet_image_enhance

Champion of the PIRM2018 Challenge on Perceptual Image Enhancement on Smartphones (Track B: Image Enhancement)

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