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

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

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

Camera-Invariant Domain Adaptation (Semantic Segmentation)

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

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

MTLA - Multi-Task Learning Archive

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

CS415 - From K-means to Deep Learning

Compact Semantic Segmentation and Depth Estimation with Multi-task Learning

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

GPU-accelerated Semantic Image Segmentation with PyTorch

CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation (ICRA2021)

PyTorch implementation for Semantic Segmentation on Cityscapes dataset using R2UNET and its modified version.

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

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