A survey of Real time Semantic Segmentation for autonomous driving

U-Net based PyTorch model for roads segmentation trained on Cityscapes dataset

Segmentation pipeline that uses a U-Net backbone to perform segmentation on the Cityscapes dataset. Conducted experiments to analyse the impact of the skip connections of the U-Net on the quality of the segmentation masks. These masks are also qualitatively analysed using the Intersection-over-Union (IoU) metric

Some basic trick about semantic segmentation based on tensorflow & some open datasets

Pytorch Implementation of U-Net on Cityscapes Dataset

DSANet: Dilated Spatial Attention for Real-time Semantic Segmentation in Urban Street Scenes

Final Project for Deep Learning Course A.Y. 2022/23. Semantic Segmentation on Cityscapes Dataset

Camera-Invariant Domain Adaptation (Semantic Segmentation)

Implementation of R2U-Net and a custom model using the main module from HANet + R2U-Net for image segmentation of urban scenes on the Cityscapes dataset

The official code open source version of BFDA - based on YOLOv5

MTLA - Multi-Task Learning Archive

This is my repository for my final project -> Semantic Segmentation of Cityscapes datasets using U-Net.

Collection of scripts for preparation of datasets for semantic segmentation of UAV images

Python program to visualize Deeplab (trained on Cityscapes dataset) results.

CS415 - From K-means to Deep Learning

Utilizing CNNs for driving scene reconstruction from single images.

Compact Semantic Segmentation and Depth Estimation with Multi-task Learning

This study investigates the performance effect of using recurrent neural networks (RNNs) for semantic segmentation of urban scene images, to generate a semantic output map with refined edges. We proposed three deep neural network architectures using recurrent neural networks and evaluated them on the Cityscapes dataset. All three proposed archit…

Corrupt Cityscapes Dataset