baoshishui's repositories
RCF
Richer Convolutional Features for Edge Detection
plot-edge-pr-curves
The code and data to plot edge PR curves for many existing edge detectors
Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》
cobnet
convolution oriented boundaries (pytorch)
DRC-Release
The code of "Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss"
test
learning github
holy-edge
Holistically-Nested Edge Detection
MTL_Segmentation
Meta Transfer Learning for Few Shot Semantic Segmentation using U-Net
Deep-Learning-with-PyTorch-Tutorials
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
OFNet
Code and model for OFNet paper
GwcNet
Group-wise Correlation Stereo Network, CVPR 2019
CASENet-1
A Pytorch implementation of CASENet for the Cityscapes Dataset
RCF-pytorch
Richer Convolutional Features for Edge Detection model in pytorch CVPR2017
BDCN
The code for the paper Bi-Directional Cascade Network for Perceptual Edge Detection( CVPR2019 ),and add the re_Dice loss from the paper Learning to Predict Crisp Boundaries (ECCV 2018)
hed
code for Holistically-Nested Edge Detection
toolbox
Piotr's Image & Video Matlab Toolbox
hello-world
just another repository
REDNet-pytorch
PyTorch Implementation of image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (NIPS 2016)
jvalidator
支持表达式，异步的表单验证器
OCD-PyTorch
The code for paper 《Object Contour Detection with a Fully Convolutional Encoder-Decoder Network》
CED
Deep Crisp Boundaries
richer-conv-feature-edge
TensorFlow implementation of Richer Convolutional Features for Edge Detection
CASENet
PyTorch implementation of CASENet
seism
Supervised Evaluation of Image Segmentation Methods
densenet-pytorch
A PyTorch Implementation for Densely Connected Convolutional Networks (DenseNets)
RCNN
The Tensorflow with tflearn implementation of the RCNN model.
Fast-edge-detection-using-structured-forests
Doll´ar, P., and Zitnick, C. L. 2015. Fast edge detection using structured forests. IEEE transactions on pattern analysis and machine intelligence 37(8):1558–1570