Good and Bad Eggs Identification Image Dataset

The Good and Bad Eggs Identification Image Dataset provides high-resolution images and detailed measurements to facilitate the classification of eggs based on quality. This dataset captures key attributes such as shell texture, color, shape, and visible defects, making it a valuable resource for food quality assessment, and deep learning applications in automated egg sorting.
Dataset Overview
• Total Images: 1,000 original images captured in real-world conditions
• Augmented Images: 6,000 (generated using data augmentation techniques)
Augmentation Pipeline
To enhance dataset diversity and improve model generalization, the following augmentation techniques were applied:
1. Horizontal flipping (50% probability)
2. Vertical flipping (30% probability)
3. Random brightness and contrast adjustments
4. Rotation within a range of -30° to +30°
5. Shear transformation along both X and Y axes
6. Addition of random noise
This dataset serves as a robust foundation for developing AI-driven quality inspection systems, enabling automated identification of defective eggs and improving food safety standards.