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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...
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 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".