# SealID: Saimaa ringed seal re-identification dataset arXiv: https://arxiv.org/abs/2206.02260 ยท HTML: https://mlsanity.com/paper/2206.02260/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 11 of its 41 references that are in the map. - First posted on arXiv 5 Jun 2022. ## Builds on (most cited first, 11 of 11) - [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017) - [Repeatability Is Not Enough: Learning Affine Regions via Discriminability](https://mlsanity.com/paper/1711.06704/) (2017) - [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018) - [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017) - [Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer](https://mlsanity.com/paper/1902.09324/) (2019) - [Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings](https://mlsanity.com/paper/1902.10847/) (2019) - [The iWildCam 2018 Challenge Dataset](https://mlsanity.com/paper/1904.05986/) (2019) - [Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales](https://mlsanity.com/paper/1708.07785/) (2017) - [Multi-views Embedding for Cattle Re-identification](https://mlsanity.com/paper/1902.04886/) (2019) - [Learning landmark guided embeddings for animal re-identification](https://mlsanity.com/paper/2001.02801/) (2020) - [EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern Matching](https://mlsanity.com/paper/2105.13979/) (2021) ## Built on by (most cited first, 22 of 22) - [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023) - [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2211.10307/) (2022) - [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024) - [WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals](https://mlsanity.com/paper/2406.09211/) (2024) - [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://mlsanity.com/paper/2407.13555/) (2024) - [Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification](https://mlsanity.com/paper/2410.06977/) (2024) - [WildFusion: Individual Animal Identification with Calibrated Similarity Fusion](https://mlsanity.com/paper/2408.12934/) (2024) - [NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation](https://mlsanity.com/paper/2206.02498/) (2022) - [Unsupervised Pelage Pattern Unwrapping for Animal Re-identification](https://mlsanity.com/paper/2506.15369/) (2025) - [GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring](https://mlsanity.com/paper/2512.07776/) (2025) - [Pairwise Matching of Intermediate Representations for Fine-grained Explainability](https://mlsanity.com/paper/2503.22881/) (2025) - [MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification](https://mlsanity.com/paper/2603.04314/) (2026) - [Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification](https://mlsanity.com/paper/2311.07407/) (2023) - [Understanding the Impact of Training Set Size on Animal Re-identification](https://mlsanity.com/paper/2405.15976/) (2024) - [Combining feature aggregation and geometric similarity for re-identification of patterned animals](https://mlsanity.com/paper/2308.06335/) (2023) - [Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats](https://mlsanity.com/paper/2406.13002/) (2024) - [Degradation-based augmented training for robust individual animal re-identification](https://mlsanity.com/paper/2603.04163/) (2026) - [Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification](https://mlsanity.com/paper/2607.09443/) (2026) - [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2311.05524/) (2023) - [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026) - [Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification](https://mlsanity.com/paper/2608.02469/) (2026) - [Cross-Species Animal Re-Identification with Semantic Consistency Learning](https://mlsanity.com/paper/2609.09705/) (2026) ## Abstract (arXiv) Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. However, massive image volumes that these methods produce are overwhelming for researchers to go through manually which calls for automatic systems to perform the analysis. The analysis task that has gained the most attention is the re-identification of individuals, as it allows, for example, to study animal migration or to estimate the population size. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in the Lake Saimaa, Finland, and is one of the few existing freshwater seal species. Ringed seals have permanent pelage patterns that are unique to each individual which can be used for the identification of individuals. Large variation in poses further exacerbated by the deformable nature of seals together with varying appearance and low contrast between the ring pattern and the rest of the pelage makes the Saimaa ringed seal re-identification task very challenging, providing a good benchmark to evaluate state-of-the-art re-identification methods. Therefore, we make our Saimaa ringed seal image (SealID) dataset (N=57) publicly available for research purposes. In this paper, the dataset is described, the evaluation protocol for re-identification methods is proposed, and the results for two baseline methods HotSpotter and NORPPA are provided. The SealID dataset has been made publicly available.