Mask2former Swin Small Ade Semantic
Small-sized Mask2Former model for ADE20k semantic segmentation based on Swin backbone network, using a unified paradigm for image segmentation tasks
Downloads 3,265
Release Time : 1/5/2023
Model Overview
Mask2Former is an advanced image segmentation model that handles instance segmentation, semantic segmentation, and panoptic segmentation tasks by predicting a set of masks and their corresponding labels. The model shows significant improvements in both performance and efficiency compared to its predecessors.
Model Features
Unified Segmentation Paradigm
Treats instance segmentation, semantic segmentation, and panoptic segmentation uniformly as instance segmentation, simplifying the task workflow
Efficient Attention Mechanism
Utilizes multi-scale deformable attention Transformer and mask attention mechanism to improve performance without increasing computational cost
Efficient Training Method
Significantly enhances training efficiency by computing loss on sampled points rather than entire masks
Model Capabilities
Image Semantic Segmentation
Instance Segmentation
Panoptic Segmentation
Use Cases
Computer Vision
Scene Understanding
Accurate segmentation and classification of objects in complex scenes
Can accurately identify and segment 150 object categories in the ADE20k dataset
Autonomous Driving
Road scene parsing, identifying vehicles, pedestrians, roads, etc.
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