Anzhcs_YOLOs
| Property | Value |
|---|---|
| Author | Anzhc |
| Model Type | YOLO Segmentation |
| Repository | Hugging Face |
| License | Not Specified |
What is Anzhcs_YOLOs?
Anzhcs_YOLOs is a comprehensive collection of YOLO-based segmentation models specialized for different body parts and features detection. These models are uniquely trained on custom-annotated datasets, offering various resolution options and specific targeting capabilities for both illustrations and real photographs.
Implementation Details
The collection includes multiple model variants trained at different resolutions (640px, 768px, 1024px) and optimized for specific tasks. The models utilize YOLO architecture with additional segmentation capabilities, achieving impressive mAP scores ranging from 0.72 to 0.87 for various tasks.
- Face segmentation models with universal application (mAP 50: 0.872)
- Gendered face detection models specifically for real photographs
- Specialized eye segmentation for anime illustrations
- Head and hair segmentation models for automated inpainting
- Breast segmentation models optimized for illustration detection
Core Capabilities
- High-accuracy face detection and segmentation across both illustrations and real photos
- Gender-specific face detection for real photographs
- Precise anime eye detection focusing on sclera area
- Combined head and hair segmentation for inpainting applications
- Specialized breast segmentation for anime-style illustrations
Frequently Asked Questions
Q: What makes this model unique?
These models are trained on carefully curated and manually annotated datasets, providing specialized segmentation capabilities for specific use cases in both illustration and real-world domains. The availability of different resolution variants allows users to balance between performance and accuracy.
Q: What are the recommended use cases?
The models are ideal for automated image editing pipelines, particularly in applications involving face modification, eye inpainting, head/hair editing, and illustration processing. They're especially useful in AI-assisted art creation and image manipulation workflows.
