ZhengPeng7/BiRefNet
BiRefNet
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.
Features
- Image Segmentations - Implements a deep learning model designed to partition images into high-resolution foreground and background masks.
- Refinement Networks - Implements a bi-directional refinement network to iteratively improve the precision of segmentation mask boundaries.
- Camouflaged Object Detectors - Provides specialized tools for identifying and segmenting objects that are hidden or blended into their environments.
- Image Segmentation - Extracts high-precision masks to isolate a single object from its background.
- Binary Segmentations - Provides supervised binary segmentation to distinguish a single foreground object from its background.
- Binary Mask Generators - Generates precise pixel-level binary masks for high-resolution industrial and medical imagery.
- Encoder-Decoder Architectures - Utilizes an encoder-decoder structure to extract semantic features and restore spatial resolution for masking.
- PyTorch Semantic Segmentation Libraries - Provides a PyTorch-based framework for training and evaluating high-precision binary image segmentation.
- Vision Model Fine-Tuning - Supports adapting the pretrained segmentation model to specialized professional use cases using custom image and mask datasets.
- AI Foreground Isolation - Extracts high-resolution masks to isolate primary subjects from complex backgrounds.
- Segmentation Model Training - Supports fine-tuning segmentation models on custom user-provided image and mask datasets.
- Feature Map Upsamplers - Uses bilinear interpolation to restore spatial resolution of feature maps during the decoding process.
- ONNX Model Exporters - Transforms trained model weights into the standardized ONNX format for cross-platform deployment.
- ONNX Model Exports - Enables conversion of vision models into the ONNX format for cross-engine compatibility.
- Quality Defect Detection - Provides automated segmentation to detect defect patterns and anomalies in manufacturing components during the production process.
- Model Weight Conversions - Serializes model weights into the ONNX format to enable high-performance hardware-accelerated inference.
- High-Resolution Masking Pipelines - Produces precise binary masks for complex, high-resolution images used in industrial and medical contexts.
