B

Birefnet DIS5K TR TEs

Developed by ZhengPeng7
BiRefNet is a high-resolution binary image segmentation model based on bilateral reference, specifically designed for background removal and mask generation tasks.
Downloads 529
Release Time : 8/1/2024

Model Overview

BiRefNet is an efficient image segmentation model focused on high-resolution binary image segmentation tasks, capable of accurately separating foreground objects from complex backgrounds.

Model Features

High-resolution processing
Capable of handling high-resolution images while preserving detail integrity.
Bilateral reference mechanism
Employs a bilateral reference mechanism to enhance segmentation accuracy.
Efficient segmentation
Efficiently and accurately separates foreground objects even in complex backgrounds.

Model Capabilities

Background removal
Mask generation
High-resolution image segmentation

Use Cases

Image editing
Background replacement
Used for background replacement in photo editing, precisely separating foreground objects.
Generates high-quality masks for subsequent editing.
Object extraction
Extracts specific objects from complex backgrounds.
Accurately segments object edges, reducing manual intervention.
Computer vision
Preprocessing for object detection
Used as preprocessing for object detection tasks to improve detection accuracy.
Reduces background interference, enhancing the accuracy of object detection.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase