Mask2former Swin Large Mapillary Vistas Semantic
A large-scale Mask2Former model based on the Swin backbone network, designed for general image segmentation tasks, unifying instance segmentation, semantic segmentation, and panoptic segmentation.
Downloads 5,539
Release Time : 1/5/2023
Model Overview
Mask2Former is an advanced image segmentation model that addresses instance segmentation, semantic segmentation, and panoptic segmentation tasks in a unified manner by predicting a set of masks and their corresponding labels. Compared to previous models, it offers significant improvements in both performance and efficiency.
Model Features
Unified Segmentation Framework
Unifies instance segmentation, semantic segmentation, and panoptic segmentation as a mask prediction problem, simplifying task processing.
Efficient Attention Mechanism
Uses a multi-scale deformable attention Transformer to replace traditional pixel decoders, improving computational efficiency.
Masked Attention Decoder
Introduces a Transformer decoder with masked attention, enhancing performance without increasing computational load.
Efficient Training Strategy
Calculates loss based on sampled points rather than full masks, significantly improving training efficiency.
Model Capabilities
Semantic Segmentation
Instance Segmentation
Panoptic Segmentation
Image Understanding
Scene Parsing
Use Cases
Autonomous Driving
Road Scene Understanding
Identifies and segments various elements in road scenes (vehicles, pedestrians, traffic signs, etc.)
Provides precise segmentation of scene elements to support autonomous driving decisions.
Remote Sensing Image Analysis
Land Cover Classification
Segments and classifies different land cover types in satellite or aerial images.
Accurately identifies and segments various land cover types, supporting land use analysis.
Medical Imaging
Organ Segmentation
Segments specific organs or lesion areas in medical images.
Provides precise organ boundary identification to assist in diagnosis and treatment.
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