Birefnet Lite 2K
A bilateral reference framework for high-resolution binary image segmentation, focusing on background removal and mask generation tasks
Downloads 3,400
Release Time : 9/24/2024
Model Overview
BiRefNet is a deep learning model for high-resolution binary image segmentation, particularly excelling in tasks such as background removal, mask generation, camouflaged object detection, and salient object detection. The model is trained at 2K resolution, making it suitable for scenarios requiring fine segmentation.
Model Features
High-resolution processing
Optimized specifically for 2K resolution (2560Ã1440), capable of handling fine segmentation tasks for high-resolution images
Multi-dataset training
Trained on multiple high-quality datasets including DIS5K, HRS10K, and UHRSD to enhance model generalization
Bilateral reference framework
Utilizes a unique bilateral reference architecture to improve segmentation accuracy and edge detail processing
Lightweight design
Offers a lite version that reduces computational resource requirements while maintaining performance
Model Capabilities
Background removal
Mask generation
Binary image segmentation
Camouflaged object detection
Salient object detection
Use Cases
Image editing
Product image background removal
Used for background removal and replacement of product images on e-commerce platforms
Achieves a maxFm score of 0.867 on the DIS-VD dataset
Portrait processing
Portrait matting
Used for precise segmentation of portraits in post-processing photography
Achieves a maxFm score of 0.993 on the TE-P3M-500-NP dataset
Medical image analysis
Medical image segmentation
Used for segmentation of specific tissues in CT/MRI images
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