{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spots-10-animal-pattern-benchmark-dataset-for","title":"SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms","arxiv_id":"2410.21044","date":"2024-10-28","proceeding":null,"authors":["John Atanbori"],"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.","url_abs":"https://arxiv.org/abs/2410.21044v1","url_pdf":"https://arxiv.org/pdf/2410.21044v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spots-10-animal-pattern-benchmark-dataset-for","repo_url":"https://github.com/amotica/spots-10","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"DenseNet121 Distiller","rank_in_archive_order":1,"of":9,"metrics":{"Accuracy":"81.84"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"ResNet101V2 Distiller","rank_in_archive_order":2,"of":9,"metrics":{"Accuracy":"80.29"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"ResNet50V2 Distiller","rank_in_archive_order":3,"of":9,"metrics":{"Accuracy":"79.03"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"MobileNet Distiller","rank_in_archive_order":4,"of":9,"metrics":{"Accuracy":"78.26"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"MobileNetV3Small Distiller","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy":"78.04"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"MobileNetV3Large Distiller","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy":"77.88"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"NASNetMobile Distiller","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"77.75"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"MobileNetV2 Distiller","rank_in_archive_order":8,"of":9,"metrics":{"Accuracy":"77.53"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-spot-10","task":"Classification","dataset":"SPOT-10","model":"ResNet50 Distiller","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy":"77.45"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}