Corals Classification Dataset

This enhanced dataset facilitates the binary classification of coral health, distinguishing between healthy and bleached corals—a crucial indicator in coral reef ecosystem management. Initially sourced to overcome the lack of image diversity and pre-sorted training categories, this dataset now features:

• Total Images: 10,292, meticulously augmented and categorized into training, testing, and validation sets.
• Image Distribution: Training (7,384 images), Testing (923 images), Validation (985 images).
• Data Splitting: Strategically split in an 80-10-10 ratio to optimize machine learning outcomes.

Purpose and Impact:

This dataset aids in monitoring coral health, assessing environmental impacts, and enhancing conservation strategies. By providing a clear differentiation between bleached and healthy corals, it supports scientific research, conservation planning, and raises awareness about coral ecosystems’ vulnerabilities and the necessity for sustainable interventions.

Image Diversity

The dataset includes thousands of images captured from different marine environments around the world. These images showcase a wide range of coral types, colors, and structures, ensuring a diverse representation of coral biodiversity.

Detailed Annotations

Each image in the dataset is meticulously labeled with comprehensive details, including species information, habitat descriptions, and health status. This detailed annotation enables robust machine learning applications and accurate research outcomes.

Applications

This File is crucial for multiple applications, such as coral conservation efforts, marine biology studies, and the development of automated coral monitoring systems. By providing detailed and diverse data, this dataset aids in the understanding and preservation of these vital marine ecosystems, contributing significantly to global coral conservation initiatives.