Paimon Dataset YOLO Detection

Dataset Features:

• Image Format: .jpg files in 640×320 resolution.
• Annotation Format: .txt files in YOLO format, containing bounding box data with:
  • class_id
  • x_center
  • y_center
  • width
  • height

Use Cases:

• Character Detection in Games: Train YOLO models to detect and identify in-game characters or NPCs.
• Gaming Analytics: Improve recognition of specific game elements for AI-powered game analytics tools.
• Research: Contribute to academic research focused on object detection or computer vision in animated and gaming environments.

Data Structure:

• Images: High-quality .jpg images captured from multiple perspectives, ensuring robust model training across various orientations and lighting scenarios.
• Annotations: Each image has an associated .txt file that follows the YOLO format. The annotations are structured to include class identification, object location (center coordinates), and bounding box dimensions.

Key Advantages:

• Varied Angles and Scales: The dataset includes Paimon from multiple perspectives, aiding in creating more versatile and adaptable object detection models.
• Real-World Scenario: Extracted from actual gameplay footage, the dataset simulates real-world detection challenges such as varying backgrounds, motion blur, and changing character scales.
• Training Ready: Suitable for training YOLO models and other deep learning frameworks that require object detection capabilities.