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Mask2former Swin Base Coco Instance

Developed by facebook
Mask2Former model based on Swin backbone, specifically designed for COCO instance segmentation tasks, using a unified framework to handle segmentation tasks
Downloads 3,249
Release Time : 11/28/2022

Model Overview

Mask2Former is an advanced image segmentation model that achieves unified processing of instance segmentation, semantic segmentation, and panoptic segmentation by predicting a set of masks and corresponding labels. Compared to previous models, it shows significant improvements in both performance and efficiency.

Model Features

Unified Segmentation Framework
Unifies instance segmentation, semantic segmentation, and panoptic segmentation as instance segmentation tasks
Multi-scale Deformable Attention
Uses multi-scale deformable attention Transformer to replace traditional pixel decoders, improving performance
Masked Attention Mechanism
Introduces a Transformer decoder with masked attention, enhancing performance without increasing computational cost
Efficient Training Strategy
Significantly improves training efficiency by computing loss on sampled points rather than entire masks

Model Capabilities

Image Segmentation
Instance Recognition
Object Detection

Use Cases

Computer Vision
Object Instance Segmentation
Identify and segment individual object instances in images
Accurately segments common objects in the COCO dataset
Scene Understanding
Analyze multiple objects and their relationships in complex scenes
Suitable for autonomous driving, robotic vision, and other scenarios
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