This is the official repository for our recent work: PIDNet
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Updated
Dec 18, 2025 - Python
This is the official repository for our recent work: PIDNet
A pytorch-based real-time segmentation model for autonomous driving
Experiments with UNET/FPN models and cityscapes/kitti datasets [Pytorch]
Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron
A Pytorch implementation of CASENet for the Cityscapes Dataset
Official Detectron2 implementation of DA-RetinaNet, An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites, Image and Vision Computing (IMAVIS) 2021
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild, Computer Vision and Pattern Recognition (CVPR) 2018
Repository for "Stochastic Segmentation with Conditional Categorical Diffusion Models" (ICCV 2023)
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper "Calibrated Adversarial Refinement for Stochastic Semantic Segmentation"
[ICIP 2019] : Official PyTorch implementation of the paper "What's There in The Dark" accepted in IEEE International Conference in Image Processing 2019 (ICIP19) , Taipei, Taiwan.
TensorFlow implementation of a comprehensive comparison of various SSL (Semi-Supervised Learning) approaches in image segmentation, featuring our novel Inconsistency Masks (IM) method.
CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation (ICRA2021)
Official Repository for BEVANet: Bilateral Efficient Visual Attention Network for Real-time Semantic Segmentation (ICIP 2025 Spotlight Oral)
Collection of scripts for preparation of datasets for semantic segmentation of UAV images
DSANet: Dilated Spatial Attention for Real-time Semantic Segmentation in Urban Street Scenes
This repository contains the official implementation of P2AT, a novel architecture designed for real-time semantic segmentation. P2AT achieves trade-off between accuracy and speed, establishing state-of-the-art results on Cityscapes and CamVid (pretrained on Cityscapes) without relying on inference acceleration techniques.
A survey of Real time Semantic Segmentation for autonomous driving
Python program to visualize Deeplab (trained on Cityscapes dataset) results.
Utilizing CNNs for driving scene reconstruction from single images.
The official code open source version of BFDA - based on YOLOv5
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