YOLOv8-Face-Detection
| Property | Value |
|---|---|
| License | AGPL-3.0 |
| Framework | PyTorch (Ultralytics) |
| Training Dataset | 10k+ face images |
| Training Hardware | NVIDIA V100 16GB GPU |
What is YOLOv8-Face-Detection?
YOLOv8-Face-Detection is a specialized computer vision model designed for robust face detection tasks. Built on the powerful YOLOv8 architecture, this model has been fine-tuned on a diverse dataset of over 10,000 images containing human faces. The training process involved 100 epochs with a batch size of 16, utilizing a high-performance NVIDIA V100 GPU over approximately 140 minutes.
Implementation Details
The model leverages the Ultralytics implementation of YOLOv8 and can be easily integrated into existing pipelines using the provided Python interface. It's optimized for both accuracy and speed in face detection scenarios.
- Fine-tuned for 100 epochs
- Batch size of 16
- Training completed in ~140 minutes
- Implemented using Ultralytics framework
Core Capabilities
- Real-time face detection in images
- Extensible for face recognition tasks
- Compatible with custom dataset fine-tuning
- Efficient inference with PyTorch backend
Frequently Asked Questions
Q: What makes this model unique?
This model combines the speed and accuracy of YOLOv8 with specialized face detection capabilities, trained on a large-scale dataset of human faces. It's particularly valuable for real-world applications requiring robust face detection.
Q: What are the recommended use cases?
The model is ideal for face detection in surveillance systems, photo organization applications, and as a foundation for face recognition systems. It can be used directly for face detection or fine-tuned further for specific use cases.
