# Understanding the Impact of Training Set Size on Animal Re-identification

arXiv: https://arxiv.org/abs/2405.15976 · HTML: https://mlsanity.com/paper/2405.15976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 16 of 16)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017)
- [Keypoint-Aligned Embeddings for Image Retrieval and Re-identification](https://mlsanity.com/paper/2008.11368/) (2020)
- [Automated Visual Fin Identification of Individual Great White Sharks](https://mlsanity.com/paper/1609.06323/) (2016)
- [EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern Matching](https://mlsanity.com/paper/2105.13979/) (2021)
- [NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation](https://mlsanity.com/paper/2206.02498/) (2022)
- [Combining feature aggregation and geometric similarity for re-identification of patterned animals](https://mlsanity.com/paper/2308.06335/) (2023)

## Built on by (most cited first, 2 of 2)

- [Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings](https://mlsanity.com/paper/2507.02403/) (2025)
- [BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks](https://mlsanity.com/paper/2511.16315/) (2025)

## Abstract (arXiv)

Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual body markings, such as fur patterns or scars, and typically employ one of two strategies: local features or end-to-end learning. In this study, we delve into the impact of training set size by conducting comprehensive experiments across six different methods and five animal species. While it is well known that end-to-end learning-based methods surpass local feature-based methods given a sufficient amount of good-quality training data, the challenge of gathering such datasets for wildlife animals means that local feature-based methods remain a more practical approach for many species. We demonstrate the benefits of both local feature and end-to-end learning-based approaches and show that species-specific characteristics, particularly intra-individual variance, have a notable effect on training data requirements.