The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

Official PyTorch implementation of SegFormer

Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes

Add bisenetv2. My implementation of BiSeNet

OneFormer: One Transformer to Rule Universal Image Segmentation, arxiv 2022 / CVPR 2023

Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)

PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help

SOTA Semantic Segmentation Models in PyTorch

LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

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)

ICCV2021 (Oral) - Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Understanding Convolution for Semantic Segmentation

[CVPR 2021] Self-supervised depth estimation from short sequences

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)

[ICLR 2020] "FasterSeg: Searching for Faster Real-time Semantic Segmentation" by Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

Papers and Benchmarks about semantic segmentation, instance segmentation, panoptic segmentation and video segmentation

Official PyTorch implementation of Fully Attentional Networks

Pytorch code for semantic segmentation using ERFNet

[CVPR'22] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

This repository contains the source code of our work on designing efficient CNNs for computer vision