Segformer B4 512x512 Ade 160k
A semantic segmentation model based on the Segformer architecture, trained on the ADE20K dataset, supporting 512x512 resolution image input
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Release Time : 11/29/2024
Model Overview
This is a semantic segmentation model based on the Segformer architecture, specifically designed for image segmentation tasks, capable of identifying and segmenting 150 different categories in images.
Model Features
Efficient Segmentation
Utilizes the Segformer architecture, combining the advantages of Transformer and convolutional networks for efficient and accurate image segmentation
Pre-trained Support
Provides pre-trained model weights for direct inference or fine-tuning
High-Resolution Processing
Supports 512x512 resolution image input, suitable for high-resolution image processing
Model Capabilities
Image Semantic Segmentation
Multi-category Recognition
High-Resolution Image Processing
Use Cases
Computer Vision
Scene Understanding
Used for scene understanding in autonomous driving or robot navigation
Accurately identifies and segments 150 classes of objects such as roads, buildings, and pedestrians
Medical Image Analysis
Used for organ or lesion segmentation in medical images
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