SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms

TL;DR

The paper introduces SPOTS-10, a large grayscale image dataset of animal patterns for evaluating machine learning algorithms in night-time recognition tasks, addressing the challenge of pattern recognition without color information.

Contribution

It provides a new extensive dataset specifically designed for animal pattern recognition in night images, facilitating research in wildlife monitoring and conservation.

Findings

Dataset contains 50,000 images across 10 species.

Designed for evaluating pattern recognition algorithms.

Supports night-time wildlife identification.

Abstract

Recognising animals based on distinctive body patterns, such as stripes, spots, or other markings, in night images is a complex task in computer vision. Existing methods for detecting animals in images often rely on colour information, which is not always available in night images, posing a challenge for pattern recognition in such conditions. Nevertheless, recognition at night-time is essential for most wildlife, biodiversity, and conservation applications. The SPOTS-10 dataset was created to address this challenge and to provide a resource for evaluating machine learning algorithms in situ. This dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species. Specifically, SPOTS-10 contains 50,000 32 x 32 grayscale images, divided into ten categories, with 5,000 images per category. The training set comprises 40,000 images, while the test set contains 10,000 images. The SPOTS-10 dataset is freely available on the project GitHub page: https://github.com/Amotica/SPOTS-10.git by cloning the repository.