Face Parsing Model
| Property | Value |
|---|---|
| Parameter Count | 84.6M |
| Model Type | Transformer-based Semantic Segmentation |
| Architecture | SegFormer (Fine-tuned nvidia/mit-b5) |
| License | Non-commercial research and educational purposes |
| Research Paper | SegFormer Paper |
What is face-parsing?
Face parsing is an advanced computer vision task that involves segmenting facial images into distinct semantic regions. This model, developed by Jonathan Dinu, utilizes the SegFormer architecture to identify and segment 19 different facial features, including skin, eyes, eyebrows, mouth, and accessories.
Implementation Details
The model is built upon the nvidia/mit-b5 architecture and fine-tuned on the CelebAMask-HQ dataset. It supports multiple deployment options, including Python with PyTorch and browser-based implementation via Transformers.js. The model operates with F32 tensor type and provides comprehensive segmentation capabilities.
- Supports both CPU and GPU inference
- Includes ONNX optimization for web deployment
- Features 19 distinct segmentation labels
- Includes built-in image preprocessing capabilities
Core Capabilities
- Precise facial feature segmentation including skin, eyes, eyebrows, and more
- Support for both local and web-based inference
- Integration with popular frameworks (PyTorch, Transformers.js)
- Real-time processing capabilities
- High-resolution output maintaining input image dimensions
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of SegFormer architecture with specialized facial feature recognition, offering high-precision segmentation across 19 different facial components. Its versatility in deployment options and optimization for web use makes it particularly valuable for both research and application development.
Q: What are the recommended use cases?
The model is ideal for facial analysis applications, virtual try-on systems, facial editing software, and research in computer vision. It's particularly suited for applications requiring detailed facial feature segmentation, though limited to non-commercial use.
