Lei He's repositories
tensorflow-vgg
Tensorflow implementation of VGG 16 and VGG 19
AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
awesome-semantic-segmentation
awesome-semantic-segmentation
caffe-segnet-cudnn5
This repository was a fork of BVLC/caffe and includes the upsample, bn, dense_image_data and softmax_with_loss (with class weighting) layers of caffe-segnet (https://github.com/alexgkendall/caffe-segnet) to run SegNet with cuDNN version 5.
CASIA-Thesis
自动化所硕博论文模板
cityscapesScripts
README and scripts for the Cityscapes Dataset
coco-json-converter
COCO Json converter python script for DAVIS 2016
dataset-api
Api for visualize sample data, evaluation of different tasks
Deeplab-v2--ResNet-101--Tensorflow
An (re-)implementation of DeepLab v2 (ResNet-101) in TensorFlow for semantic image segmentation on the PASCAL VOC 2012 dataset.
deeplab_v2
基于v2版本的deeplab,使用VGG16模型,在VOC2012,Pascal-context,NYU-v2等多个数据集上进行训练
densenet-tensorflow
DenseNet Implementation in Tensorflow
DSAC
Code for DSAC (Differentiable RANSAC) for Camera Localization, CVPR 17
lanenet-lane-detection
Implemention of lanenet model for real time lane detection using deep neural network model
LapSRN
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (CVPR 2017)
leiup.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
models
Models and examples built with TensorFlow
monodepth
Unsupervised single image depth prediction with CNNs
MultiObjectiveOptimization
Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"
MVSNet
MVSNet: Depth Inference for Unstructured Multi-view Stereo (ECCV2018, Oral Presentaion)
PSPNet
Pyramid Scene Parsing Network
SfMLearner
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
sparse-to-dense
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image