Mask2former Deployment
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Mask2former Deployment
Developed by saninmohammedn
A fine-tuned semantic segmentation model based on the Mask2Former framework, suitable for road scene understanding and autonomous driving applications
Downloads 229
Release Time : 1/13/2025
Model Overview
This model is a universal mask prediction framework optimized for semantic segmentation tasks, capable of predicting segmentation masks for input images.
Model Features
Universal Mask Prediction Framework
Supports various segmentation tasks, including semantic segmentation, instance segmentation, and panoptic segmentation
Based on Swin-Large Backbone Network
Utilizes a powerful vision Transformer architecture to deliver high-quality segmentation results
Optimized for Road Scenes
Fine-tuned specifically for road scene understanding and autonomous driving applications
Model Capabilities
Image Segmentation
Semantic Segmentation
Mask Prediction
Use Cases
Autonomous Driving
Road Scene Understanding
Identify and segment various elements on the road, such as vehicles, pedestrians, traffic signs, etc.
Provides precise semantic segmentation results to help autonomous driving systems understand the environment
Computer Vision
Image Analysis
Perform semantic segmentation on different objects within an image
Generates accurate segmentation masks for further analysis and processing
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