BiRefNet is a PyTorch image segmentation framework designed for high-precision binary mask generation. It functions as a bilateral image segmentation model used to isolate foreground objects from complex backgrounds, as well as a specialized tool for camouflaged object detection and industrial defect detection.
The project is designed for export to the ONNX format, which facilitates cross-platform deployment and inference. It supports custom model fine-tuning on user-provided image and mask datasets to adapt the model for specialized professional use cases.
The system covers high-resolution image processing for dichotomous segmentation and automated quality control for industrial inspection. It includes utilities for model accuracy evaluation using standard metrics across benchmark datasets.
