Modified state-of-the-art EfficientPS model by replacing the instance head (Mask-RCNN) with SOLOv2, making it a location based-panoptic segmentation model.

Semantic segmentation is a fundamental task in computer vision that involves labeling each pixel in an image with a specific class, enabling a detailed understanding of the image’s content. While traditional approaches relied on convolutional neural networks (CNNs) for semantic segmentation, recent advancements have introduced a novel technique...

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

CNN based deep learning project for image segmentation

Segmentation of diseases using ChestX-Ray images

Semantic Segmentation of Spleen using UNET

Semantic segmentation classifies image pixels into one or more classes which are semantically interpret able. CNNs for semantic segmentation typically use a fully convolutional network (FCN) architecture, which replaces the fully connected layers of a traditional CNN with convolutional layers.

Unofficial implementation of Region-wise over-segmentation measure (ROM) and Region-wise under-segmentation measure (RUM) from the paper "Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection"

A dataset of multiclass semantic segmentation image annotations (CVAT for Images 1.1) for the first 250 images of the "Duckietown Object Detection Dataset".

Implemented the SegNet for Semantic Segmentation Using PyTorch Framework- Deep Neural Network Course Project

Data Science Bowl 2018

PyTorch for Semantic Segmentation

Pixel segmentation of roads from dashboard camera using Fully Convolutional Network

Kaggle Machine Learning computer vision project

exercise for pytorch

Light PyTorch implementation of fast-scnn for semantic segmentation