adetailer
| Property | Value |
|---|---|
| Author | Bingsu |
| License | Apache-2.0 |
| Framework | PyTorch, Ultralytics |
| Community Rating | 508 likes |
What is adetailer?
adetailer is a sophisticated object detection and segmentation model suite built on the YOLOv8 architecture. It specializes in detecting and segmenting faces, hands, people, and clothing items in both 2D and realistic images. The model family includes various sizes (nano to medium) and capabilities, offering different trade-offs between speed and accuracy.
Implementation Details
Built using the Ultralytics framework, adetailer implements multiple YOLOv8 variants trained on diverse datasets including WIDER FACE, anime segmentation datasets, and DeepFashion2. The models achieve impressive performance metrics, with mAP50 scores ranging from 0.660 to 0.849 depending on the specific task and model size.
- Multiple model variants for different tasks (face, hand, person, clothing detection)
- Segmentation capabilities in addition to bounding box detection
- Easy integration using the Hugging Face hub and Ultralytics library
- Comprehensive dataset training including both realistic and anime-style images
Core Capabilities
- Face detection with mAP50 up to 0.748 (YOLOv9c model)
- Hand detection with mAP50 reaching 0.810
- Person segmentation with both bbox and mask predictions
- Clothing detection with 13 different clothing categories
- Support for both 2D and realistic image processing
Frequently Asked Questions
Q: What makes this model unique?
adetailer stands out for its versatility in handling multiple detection tasks with a single framework, and its ability to process both realistic and 2D/anime-style images. The comprehensive model suite offers different size variants to accommodate various computational requirements while maintaining high accuracy.
Q: What are the recommended use cases?
The model is ideal for applications requiring precise detection and segmentation of human-centric elements, including facial recognition systems, gesture control interfaces, fashion applications, and content analysis tools. It's particularly useful in scenarios requiring processing of both realistic and artistic content.
