Papers › SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms
SPOTS-10: Animal Pattern Benchmark Dataset for Machine Learning Algorithms
John Atanbori
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Classification | SPOT-10 | DenseNet121 Distiller | Accuracy | 81.84 | #1 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | ResNet101V2 Distiller | Accuracy | 80.29 | #2 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | ResNet50V2 Distiller | Accuracy | 79.03 | #3 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | MobileNet Distiller | Accuracy | 78.26 | #4 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | MobileNetV3Small Distiller | Accuracy | 78.04 | #5 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | MobileNetV3Large Distiller | Accuracy | 77.88 | #6 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | NASNetMobile Distiller | Accuracy | 77.75 | #7 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | MobileNetV2 Distiller | Accuracy | 77.53 | #8 of 9 | Archive leaderboard | report |
| Classification | SPOT-10 | ResNet50 Distiller | Accuracy | 77.45 | #9 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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