YOLOv8-Face-Detection
| Property | Value |
|---|---|
| License | AGPL-3.0 |
| Framework | PyTorch (Ultralytics) |
| Training Duration | 140 minutes |
| Dataset Size | 10,000+ images |
What is YOLOv8-Face-Detection?
YOLOv8-Face-Detection is a specialized computer vision model fine-tuned for accurate face detection tasks. Built on the powerful YOLOv8 architecture, this model has been optimized using a comprehensive dataset of over 10,000 face images, trained for 100 epochs on an NVIDIA V100 16GB GPU.
Implementation Details
The model leverages the Ultralytics implementation of YOLOv8 and was trained with a batch size of 16. It's designed for seamless integration into both detection and recognition workflows, with the training process specifically optimized for facial feature detection.
- Training Infrastructure: NVIDIA V100 16GB GPU
- Training Duration: 140 minutes
- Dataset: WIDER FACE dataset from Roboflow Universe
- Batch Size: 16
- Epochs: 100
Core Capabilities
- Real-time face detection in images
- Extensible for face recognition tasks
- Compatible with custom dataset fine-tuning
- Optimized for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model combines the speed and accuracy of YOLOv8 with specialized training for face detection, making it particularly effective for real-world applications. Its training on a diverse dataset of over 10,000 images ensures robust performance across various conditions.
Q: What are the recommended use cases?
The model is ideal for face detection in security systems, attendance systems, and social media applications. It can also serve as a foundation for face recognition systems through additional fine-tuning on labeled face datasets.
