Product Defects Dataset

Dataset Description

The Product Defects Dataset contains high-resolution images of jar lids with varying degrees of damage. The dataset is designed to aid in the development of machine learning models for defect detection and classification.

Key Features

• Total Images: 168
• Total Jar Lids: 1859 (an average of 11 jar lids per image)
• Categories:
  • Intact Jar Lids: 962
  • Damaged Jar Lids: 897
• Types of Damages:
  • Lid Deformations
  • Holes
  • Scratches

Annotations

Each image in the dataset is meticulously annotated to highlight the location and type of defect present on each jar lid. The annotations provide precise coordinates and labels for each identified defect, ensuring that the dataset is ready for use in supervised learning tasks.

Additional Information

• Image Quality: The images are captured in high resolution, ensuring that even subtle defects are visible and can be accurately detected by algorithms.
• Diversity of Damage: The dataset includes a wide range of defect types and severities, from minor scratches to significant lid deformations, providing a robust training ground for machine learning models.
• Usage Scenarios: This dataset is ideal for training computer vision models for quality control in manufacturing, automated inspection systems, and defect detection applications in various industries.