Instructions to use cvtechniques/VideoGameHandGestures with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use cvtechniques/VideoGameHandGestures with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("cvtechniques/VideoGameHandGestures") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,16 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Model Description
|
| 2 |
+
### Overview
|
| 3 |
+
This model detects hand gestures that can be used as input controls for video games. It uses object detection to recognize specific hand poses from a webcam or standard camera and translate them into game actions.
|
| 4 |
+
The goal of the project is to explore whether computer vision–based gesture recognition can provide a low-cost and accessible alternative to traditional game controllers.
|
| 5 |
+
|
| 6 |
+
### Training Approach
|
| 7 |
+
The model was trained using the nano version of YOLOv8 through the Ultralytics training framework.
|
| 8 |
+
The model was trained from pretrained YOLOv8n weights and fine-tuned on a custom hand gesture dataset.
|
| 9 |
+
|
| 10 |
+
### Intended Use Cases
|
| 11 |
+
* Gesture-controlled video games with simple control schemes
|
| 12 |
+
* Touchless interfaces
|
| 13 |
+
* Interactive displays
|
| 14 |
+
* Public kiosks
|
| 15 |
+
* Smart home media controls
|
| 16 |
+
* Desktop navigation
|