Mask2former Swin Small Cityscapes Panoptic
A compact Mask2Former model based on Swin backbone network, optimized for panoptic segmentation tasks on the Cityscapes dataset
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Release Time : 1/3/2023
Model Overview
Mask2Former is a universal image segmentation framework that unifies instance segmentation, semantic segmentation, and panoptic segmentation through predicting a set of masks and corresponding labels. This specific checkpoint is fine-tuned for urban street scene panoptic segmentation.
Model Features
Unified Segmentation Framework
Unifies instance segmentation, semantic segmentation, and panoptic segmentation into mask prediction tasks, simplifying the processing pipeline
Efficient Attention Mechanism
Uses multi-scale deformable attention Transformer to replace traditional pixel decoders, improving computational efficiency
Masked Attention Decoder
Innovatively introduces Transformer decoder with masked attention to enhance performance without increasing computational load
Efficient Training Strategy
Significantly reduces training computational resource consumption by calculating loss through sampled points rather than entire masks
Model Capabilities
Image Segmentation
Street Scene Understanding
Object Recognition and Localization
Panoptic Segmentation
Use Cases
Intelligent Transportation Systems
Street Scene Element Analysis
Accurately segments and classifies vehicles, pedestrians, traffic signs, etc. in urban roads
Can be used for traffic flow monitoring and urban planning
Autonomous Driving
Environmental Perception
Real-time identification and segmentation of various objects in road scenes
Provides precise environmental understanding for autonomous driving systems
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