BiRefNet_lite
| Property | Value |
|---|---|
| Parameter Count | 44.4M |
| Paper | Bilateral Reference for High-Resolution Dichotomous Image Segmentation |
| License | MIT |
| Author | ZhengPeng7 |
| Tags | Image Segmentation, Background Removal, Mask Generation |
What is BiRefNet_lite?
BiRefNet_lite is a lightweight version of BiRefNet, designed for high-resolution dichotomous image segmentation. It's specifically optimized for tasks like background removal, mask generation, and salient object detection, while maintaining efficient performance with only 44.4M parameters.
Implementation Details
The model implements a bilateral reference architecture for image segmentation, utilizing a Swin Transformer tiny (V1) backbone. It processes images at 1024x1024 resolution and outputs high-quality segmentation masks.
- Efficient architecture with Swin Transformer backbone
- Supports both CPU and CUDA execution
- Implements normalized image preprocessing
- Outputs binary segmentation masks
Core Capabilities
- High-resolution image segmentation
- Background removal with precision
- Camouflaged object detection
- Salient object detection
- Real-time mask generation
Frequently Asked Questions
Q: What makes this model unique?
BiRefNet_lite stands out for its efficient bilateral reference mechanism that enables high-quality segmentation while maintaining a relatively small parameter count, making it suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for applications requiring precise object segmentation, including background removal tools, image editing software, and computer vision systems needing accurate object detection and mask generation.
