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

DeepLab v3+ model in PyTorch. Support different backbones.

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

PyTorch implementation for semantic segmentation (DeepLabV3+, UNet, etc.)

LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

DeepGlobe Land Cover Classification Challenge遥感影像语义分割

DeepLabV3+ implemented in TensorFlow2.0

semantic segmentation pytorch 语义分割

Pytorch implementation and extension of "DocUnet: Document Image Unwarping via A Stacked U-Net"

Real-time semantic image segmentation on mobile devices

图像分割算法deeplab_v3+，基于tensorflow，中文注释，摄像头可用

TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation

deeplabv3plus (Google's new algorithm for semantic segmentation) in keras：Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Using deepLabv3+ to segment humans

DeepLabV3+ with squeeze and excitation network for human image segmentation in TensorFlow 2.5.0

Base on tensorrt version 8.2.4, compare inference speed for different tensorrt api.

Try to implement deeplab v3+ on pytorch according to offical demo.

3クラス(肌、服、髪)のセマンティックセグメンテーションを実施するモデル(A model that performs semantic segmentation of 3 classes(skin, clothes, hair))

Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg