Pytorch for Segmentation

Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

Datasets, Transforms and Models specific to Computer Vision

A Pytorch implementation of U-Net using a DenseNet-121 backbone

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, …

This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

Segmentation for vertebra in MR images

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Semantic Segmentation Architectures Implemented in PyTorch

Implement some models of RGB/RGBD semantic segmentation in PyTorch, easy to run. Such as FCN, RefineNet, PSPNet, RDFNet, 3DGNN, PointNet, DeepLab V3, DeepLab V3 plus, DenseASPP, FastFCN

Pytorch implementation of refinenet network

Semantic segmentation models, datasets and losses implemented in PyTorch.

CCF BCDI2017 卫星影像的AI分类与识别

多通道城市图像分类

1st Place Solution for DataCastle-CashBus Competition

R语言忍者秘笈