Mask2former Swin Base Coco Panoptic
The Mask2Former model based on the Swin backbone network, trained on the COCO panoptic segmentation dataset, adopts a unified paradigm to handle instance segmentation, semantic segmentation, and panoptic segmentation tasks.
Downloads 45.01k
Release Time : 1/2/2023
Model Overview
Mask2Former is a universal image segmentation model that unifies instance segmentation, semantic segmentation, and panoptic segmentation tasks by predicting a set of masks and their corresponding labels. It achieves breakthroughs in both performance and efficiency compared to previous models.
Model Features
Unified Segmentation Paradigm
Unifies instance segmentation, semantic segmentation, and panoptic segmentation as mask prediction problems, simplifying the task processing workflow.
Multi-scale Deformable Attention
Upgrades the pixel decoder with a multi-scale deformable attention mechanism to enhance feature extraction capabilities.
Masked Attention Decoder
Employs a transformer decoder with masked attention to improve model performance at zero computational cost.
Efficient Training Strategy
Significantly enhances training efficiency by computing losses on subsampled points rather than full masks.
Model Capabilities
Image Segmentation
Instance Segmentation
Semantic Segmentation
Panoptic Segmentation
Use Cases
Computer Vision
Scene Understanding
Accurately segments and classifies objects in complex scenes
Simultaneously identifies object instances and semantic categories
Autonomous Driving
Parses road scenes to identify vehicles, pedestrians, roads, and other elements
Provides precise object boundaries and category information
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