Description:

Classes: 87 distinct gemstone classes for accurate classification

Context: This dataset aims to assist algorithms in distinguishing between various gemstones, such as ruby, amethyst, and emerald, by providing labeled images.

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Content:

Overview: The dataset comprises over 3,200 high-quality images of gemstones, organized into 87 classes. These images are pre-sorted into training and testing sets, facilitating easy implementation in machine learning projects.

Details:

Shapes Included: The images capture gemstones in various shapes including round, oval, square, rectangle, and heart.

Structure:

Train Folder (~56 MB): Contains 87 subfolders, each representing a different class, with approximately 2,800 images in .jpeg format.

Test Folder (~8 MB): Also contains 87 subfolders, with around 400 images in total, ensuring a balanced test set for evaluating model performance.

Use Cases:

Classification Tasks: Ideal for training classification models to identify gemstone types.

Computer Vision Projects: Suitable for projects requiring detailed image analysis and pattern recognition in diverse shapes and colors.

E-commerce Applications: Can be used to develop systems for automatic gemstone recognition and cataloging in online marketplaces.

Additional Information:

File Formats: All images are provided in .jpeg format, ensuring compatibility with various image processing tools.

Data Quality: Images are carefully labeled and curated to maintain high quality and relevance.

This dataset is sourced from Kaggle.