Product Defects Dataset

Product Defects Dataset

Datasets

Product Defects Dataset

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Product Defects Dataset

Use Case

Product Dataset

Description

Explore our Product Defects Dataset, featuring high-resolution images of jar lids with detailed annotations of both intact and damaged lids.

Product Defects Dataset

Description:

Product defects represent a significant challenge for businesses, leading to potential financial losses and consumer health risks. Effective detection and categorization of these defects are crucial for maintaining product quality and customer satisfaction. This dataset focuses on jar lids, providing a comprehensive collection of images that include both intact and damaged lids, along with detailed annotations.

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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.

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