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

SOTA Semantic Segmentation Models in PyTorch

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

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

TEED: Tiny and Efficient Edge Detector

The official implementation for MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning (CVPR '24)

[SAIN'18] [Caffe] A dilated version of FCN with Stride 2 for Efficient Semantic Segmentation

A modular and extensible framework for training and evaluating semantic segmentation models with PyTorch Lightning, supporting multiple architectures, datasets, losses, and data augmentation pipelines out of the box.

Processing deep-learning datasets (i.e. coco, pascal voc, cityscapes, pascal context)

[CVPR2025] Official implementation of TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning

A pytorch implementation of Flattenet.