YOLOv8x-Tuned-Hand-Gestures
| Property | Value |
|---|---|
| Author | lewiswatson |
| Model Type | Object Detection (Hand Gestures) |
| Base Architecture | YOLOv8x |
| Training Framework | Ultralytics & Ultralyticsplus |
| Model URL | Hugging Face |
What is yolov8x-tuned-hand-gestures?
This is a specialized version of the YOLOv8x model that has been fine-tuned specifically for hand gesture recognition using the Roboflow dataset. The model leverages the powerful YOLOv8 architecture while being optimized for detecting and classifying various hand gestures in images.
Implementation Details
The model was trained using the Ultralytics and Ultralyticsplus frameworks with specific optimization parameters. Training was conducted over 10 epochs using SGD (Stochastic Gradient Descent) optimizer with a patience value of 50. The model processes images at a resolution of 640x640 pixels and utilizes dynamic batch sizing.
- Training Duration: 10 epochs
- Optimizer: SGD
- Image Size: 640x640
- Framework: Ultralytics & Ultralyticsplus
- Early Stopping Patience: 50
Core Capabilities
- Real-time hand gesture detection
- Support for various hand gesture classifications
- Easy integration with Python applications
- Efficient inference on images
- Compatible with Ultralytics ecosystem
Frequently Asked Questions
Q: What makes this model unique?
This model combines the robust YOLOv8x architecture with specialized training for hand gesture recognition, making it particularly effective for gesture detection tasks while maintaining the speed and efficiency of the YOLO family of models.
Q: What are the recommended use cases?
The model is ideal for applications requiring hand gesture recognition, such as sign language interpretation, human-computer interaction, gesture-controlled interfaces, and interactive multimedia applications. It can be easily integrated into existing systems using the Ultralytics framework.
