Good and Bad Eggs Identification Image Dataset
Description
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.