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Birefnet HRSOD
High-resolution binary image segmentation model based on bilateral reference, focusing on salient object detection and background removal tasks
Downloads 1,724
Release Time : 8/1/2024
Model Overview
BiRefNet is a deep learning model for high-resolution salient object detection (HRSOD), capable of generating precise binary image segmentation masks to achieve background removal and salient object detection functions.
Model Features
High-resolution processing capability
Optimized specifically for high-resolution images, capable of handling detailed image segmentation tasks
Bilateral reference mechanism
Adopts a bilateral reference architecture to improve segmentation accuracy and edge processing capability
Multi-dataset support
Trained on multiple datasets including DUTS/HRSOD/UHRSD, with broad applicability
Model Capabilities
Image segmentation
Salient object detection
Background removal
Mask generation
Use Cases
Image processing
Background removal
Precisely separate subjects from backgrounds in photos
Generate high-quality subject masks
Salient object detection
Identify the most salient objects or regions in an image
Accurately mark salient region boundaries
Computer vision applications
Image editing
Provide automatic segmentation functionality for image editing software
Simplify image editing workflows
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