├── README.md
└── previewImages
├── 300-Cattle-source.png
├── CoBRA.png
├── GZGC.png
├── Giraffe.png
├── MammalClub.png
├── NDD20.png
├── SeaTurtleID2022.png
├── SheepDataset.png
├── aerialCattle2017.png
├── animalFaceDataset.png
├── belugaID.png
├── birdIndividualID.png
├── c-Tai.png
├── c-Zoo.png
├── cat.jpg
├── cattleFaceDataset.png
├── chicks4freeid.png
├── cow.jpg
├── cows2021.png
├── dog.jpg
├── friesianCattle2015.png
├── friesianCattle2017.png
├── fruitFlyDataset.png
├── giantpanda.jpg
├── happywhale.jpg
├── humpbackWhaleID.png
├── hyienaID.png
├── iPanda50.png
├── idfromvideo.png
├── leopardID.png
├── lionFace.png
├── macaqueFaces.png
├── multiPoseDog.png
├── nyalaData.png
├── openCows2020.png
├── polarBearVidID.png
├── previewImages
├── rightWhaleRecognition.png
├── seaStarReID.png
├── seaTurtleIDHeads.png
├── seal.jpg
├── stripeSpotter.png
├── tiger.jpg
├── turtleRecall.png
├── whaleSharkID.png
└── zebrafish.png
/README.md:
--------------------------------------------------------------------------------
1 | # Individual Animal Re-ID Datasets
2 | > [!NOTE]
3 | > Citations go a long way — give credit where it’s due!
4 |
6 |
7 |
8 | ### Table 1: A collection of publicly available computer vision datasets for the re-identification of individual animals
9 | Arranged by date of publicatipn. An asterisk (*) marks data derived from video footage. "n.a." indicates that a corresponding publication could not be found. The number of individuals present in each dataset is listed under "IDs". "Annot\." denotes the total number of annotated instances.
10 |
11 | | **Year** | **Dataset** | **Publication** | **IDs** | **Species** | **Annot\.** | **Preview** |
12 | |----------|-------------|-----------------|---------|-------------|-----------------|-------------|
13 | | 2025 | [WildlifeReID-10k ](https://www.kaggle.com/datasets/wildlifedatasets/wildlifereid-10k/code) | [WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals](https://arxiv.org/abs/2406.09211) | 10772 | bird, cat, chicken, chimpanzee, cow, dog, dolphin, fish, giraffe, hyena, leopard, macaque, nyala, panda, polar bear, sea star, sea turtle, seal, tiger, whale, whaleshark, zebra | 140488 | species of 37 datasets from the [wildlife-datasets python library](https://github.com/WildlifeDatasets/wildlife-datasets)|
14 | | 2025 | [Cattle Face Dataset](https://pan.baidu.com/s/1wHKT-6R6B9OtxaYZ2N4MOw?pwd=6ckt) | [Enhanced multi-breed cattle face recognition in complex environments using attention-based deep learning](https://doi.org/10.1007/s00371-025-03910-z) | 1957| cattle\* (Simmental, Angus, Hereford, Limousin, Charolais, Piedmontese, Sanhe, Japanese Wagyu) | 14047 | |
15 | | 2025 | [CoBRA re identidication youngstock](https://zenodo.org/records/15018518) | [Dynamic Multi-Behaviour, Orientation-Invariant Re-Identification of Holstein-Friesian Cattle](https://doi.org/10.3390/s25102971) | 48 | (red) Holstein-Friesian cattle\* | 11438 | |
16 | | 2024 | [Chicks4FreeID](https://doi.org/10.57967/hf/2345) | [Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification](https://doi.org/10.3390/ani15010001) | 50,
2,
2 | chicken,
duck,
rooster | 1215,
40,
15 | |
17 | | 2024 | [SeaTurtleID2022](https://www.kaggle.com/datasets/wildlifedatasets/seaturtleid2022) | [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://doi.org/10.1109/WACV57701.2024.00699) | 438 | sea turtle | 8729 ||
18 | | 2024 | [SheepDataset](https://pan.baidu.com/s/1EOIO4C4c1VxF5UIEaOXkrQ?pwd=13no) | [SheepNet: Rapid Sheep Face Recognition Based on Attention and Knowledge Distillation](https://doi.org/10.1007/978-981-97-8502-5_18) | 56 | Small-tailed Han sheep\* | 5285 ||
19 | | 2023 | [Mammal Club
\(IISD\)](https://github.com/WJ-0425/MammalClub) | [MammalClub: An Annotated Wild Mammal Dataset for Species Recognition, Individual Identification, and Behavior Recognition](https://doi.org/10.3390/electronics12214506) | 218 | 11 terrestrial mammal species\* | 33612 ||
20 | | 2023 | [Multi\-pose
dog dataset](https://data.mendeley.com/datasets/v5j6m8dzhv/1) | [Animal Re-Identification Algorithm for Posture Diversity](https://doi.org/10.1109/ICASSP49357.2023.10094783) | 192 | dog | 1657 ||
21 | | 2023 | [PolarBearVidID](https://doi.org/10.5281/zenodo.7564529) | [PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears](https://doi.org/10.3390/ani13050801) | 13 | polar bear\* | 138363 | |
22 | | 2023 | [Sea Star Re\-ID](https://lila.science/sea-star-re-id-2023/) | [An open-source general purpose machine learning framework for individual animal re-identification using few-shot learning](https://doi.org/10.1111/2041-210X.14278) | 39,
56 | common starfish,
Australian cushion star | 1204,
983 ||
23 | | 2022 | [Animal\-
Identification\-
from\-Video](https://github.com/LucyKuncheva/Animal-Identification-from-Video) | [A Benchmark Database for Animal Re-Identification and Tracking](https://doi.org/10.1109/IPAS55744.2022.10052988) | 58,
26,
9 | pigeon\*,
pig\*,
Koi fish\* | 12671,
6184,
1635 |  |
24 | | 2022 | [Beluga ID](https://lila.science/datasets/beluga-id-2022/) | n\.a\. | 788 | beluga whale | 5902 ||
25 | | 2022 | [Happywhale](https://kaggle.com/competitions/happy-whale-and-dolphin) | n\.a\. | 15587 | 30 different species of whales and dolphins | 51033 ||
26 | | 2022 | [Hyiena ID](https://lila.science/datasets/hyena-id-2022/) | n\.a\. | 256 | spotted hyena | 3129 ||
27 | | 2022 | [Leopard ID](https://lila.science/datasets/leopard-id-2022/) | n\.a\. | 430 | African leopard | 6805 ||
28 | | 2022 | [SealID](https://doi.org/10.23729/0f4a3296-3b10-40c8-9ad3-0cf00a5a4a53) | [SealID: Saimaa Ringed Seal Re-Identification Dataset](https://doi.org/10.3390/s22197602) | 57 | Saimaa ringed seal | 2080 | |
29 | | 2022 | [SeaTurtleIDHeads](https://www.kaggle.com/datasets/wildlifedatasets/seaturtleidheads) | [SeaTurtleID: A novel long-span dataset highlighting the importance of timestamps in wildlife re-identification](https://doi.org/10.48550/arXiv.2211.10307) | 400 | sea turtle | 7774 ||
30 | | 2022 | [Turtle Recall](https://zindi.africa/competitions/turtle-recall-conservation-challenge/data) | n\.a\. | 100 | sea turtle | 2145 ||
31 | | 2021 | [Cow Dataset](https://doi.org/10.6084/m9.figshare.16879780) | [Individual dairy cow identification based on lightweight convolutional neural network](https://doi.org/10.1371/journal.pone.0260510) | 13 | cow | 3772 |  |
32 | | 2021 | [Cows2021](https://doi.org/10.5523/bris.4vnrca7qw1642qlwxjadp87h7) | [Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset](https://doi.org/10.48550/arXiv.2105.01938) | 182 | Holstein\-Friesian cattle\* | 13784 |  |
33 | | 2021 | [Giraffe Dataset](https://plmlab.math.cnrs.fr/vmiele/animal-reid/) | [Revisiting animal photo-identification using deep metric learning and network analysis](https://doi.org/10.1111/2041-210X.13577) | 62 | giraffe | 624 ||
34 | | 2021 | [iPanda\-50](https://github.com/iPandaDateset/iPanda-50) | [Giant Panda Identification](https://doi.org/10.1109/TIP.2021.3055627) | 50 | giant panda | 6874 |  |
35 | | 2021 | [300-Cattle-source](https://cloud.une.edu.au/index.php/s/eMwaHAPK08dCDru) | [Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle](https://doi.org/10.3390/agronomy11112365 ) | 300 | Bos taurus beef cattle (including Angus, Hereford Charolais and Simmenta) | 2900 |  |
36 | | 2020 | [AAU Zebrafish Dataset](https://www.kaggle.com/datasets/aalborguniversity/aau-zebrafish-reid) | [Re-Identification of Zebrafish using Metric Learning](https://doi.org/10.1109/WACVW50321.2020.9096922) | 6 | zebrafish\* | 6672 |  |
37 | | 2020 | [Animal Face
Dataset](https://doi.org/10.17632/z3x59pv4bz.2) | [Automatic Identification of Individual Primates with Deep Learning Techniques](https://doi.org/10.1016/j.isci.2020.101412) | 1040 | 41 primate species | 102399 ||
38 | | 2020 | [ATRW](https://lila.science/datasets/atrw) | [ATRW: A Benchmark for Amur Tiger Re-identification in the Wild](https://doi.org/10.1145/3394171.3413569) | 92 | Amur tiger\* | 3649 |  |
39 | | 2020 | [Lion Face
Dataset](https://github.com/tvanzyl/wildlife_reidentification/) | [Automated Identification of Individuals in Wildlife Population Using Siamese Neural Networks](https://doi.org/10.1109/ISCMI51676.2020.9311574) | 94 | lion | 740 ||
40 | | 2020 | [NDD20](https://doi.org/10.25405/data.ncl.c.4982342) | [NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation](https://doi.org/10.48550/arXiv.2005.13359) | 44,
82 | bottlenose and white\-beaked dolphin,
white\-beaked dolphin \(underwater\)\* | 2201,
2201 ||
41 | | 2020 | [Nyala Data](https://github.com/tvanzyl/wildlife_reidentification/) | [Automated Identification of Individuals in Wildlife Population Using Siamese Neural Networks](https://doi.org/10.1109/ISCMI51676.2020.9311574) | 237 | nyala | 1942 ||
42 | | 2020 | [OpenCows2020](https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml17) | [Visual identification of individual Holstein-Friesian cattle via deep metric learning](https://doi.org/10.1016/j.compag.2021.106133) | 46 | Holstein\-Friesian cattle\* | 4736 ||
43 | | 2019 | [Bird individualID](https://github.com/AndreCFerreira/Bird_individualID) | [Deep learning-based methods for individual recognition in small birds](https://doi.org/10.1111/2041-210X.13436) | 30,
10,
10 | sociable weaver,
great tit,
zebra finch | 51934 ||
44 | | 2019 | [Dog Face
Dataset](https://github.com/GuillaumeMougeot/DogFaceNet)| [A Deep Learning Approach for Dog Face Verification and Recognition](https://doi.org/10.1007/978-3-030-29894-4_34) | 1393 | dog | 8363 |  |
45 | | 2018 | [Cat Individual
Images](https://www.kaggle.com/datasets/timost1234/cat-individuals) | [Cat face recognition using deep learning](http://dx.doi.org/10.13031/aim.201800316) | 518 | cat | 13536 |  |
46 | | 2018 | [Fruit Fly Dataset](https://doi.org/10.5683/SP2/JP4WDF) | [Can Drosophila melanogaster tell who’s who?](https://doi.org/10.1371/journal.pone.0205043) | 60 | fruit fly\* | 2592000 ||
47 | | 2018 | [HumpbackWhaleID](https://kaggle.com/competitions/humpback-whale-identification) | n\.a\. | 5004 | humpback whale | 15697 ||
48 | | 2018 | [MacaqueFaces](https://github.com/clwitham/MacaqueFaces) | [Automated face recognition of rhesus macaques](https://doi.org/10.1016/j.jneumeth.2017.07.020) | 34 | rhesus macaque\* | 6280 |  |
49 | | 2017 | [AerialCattle2017](https://doi.org/10.5523/bris.3owflku95bxsx24643cybxu3qh) | [Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning](https://doi.org/10.1109/ICCVW.2017.336) | 23 | Holstein\-Friesian cattle\* | 46340 ||
50 | | 2017 | [FriesianCattle2017](https://doi.org/10.5523/bris.2yizcfbkuv4352pzc32n54371r) | [Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning](https://doi.org/10.1109/ICCVW.2017.336) | 89 | Holstein\-Friesian cattle\* | 940 ||
51 | | 2017 | [GZGC](https://lila.science/datasets/great-zebra-giraffe-id) | [Animal population censusing at scale with citizen science and photographic identification](http://arks.princeton.edu/ark:/88435/pr1s791) | 2056 | plains zebra and Masai giraffe | 6925 ||
52 | | 2016 | [C\-Tai](https://github.com/cvjena/chimpanzee_faces) | [Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates](https://doi.org/10.1007/978-3-319-45886-1_5) | 78 | chimpanzee | 5078 ||
53 | | 2016 | [C\-Zoo](https://github.com/cvjena/chimpanzee_faces) | [Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates](https://doi.org/10.1007/978-3-319-45886-1_5) | 24 | chimpanzee | 2109 ||
54 | | 2016 | [FriesianCattle2015](https://doi.org/10.5523/bris.wurzq71kfm561ljahbwjhx9n3) | [Automatic individual Holstein Friesian cattle identification via selective local coat pattern matching in RGB-D imagery](https://doi.org/10.1109/ICIP.2016.7532404) | 40 | Holstein\-Friesian cattle\* | 377 ||
55 | | 2015 | [Right Whale
Recognition](https://kaggle.com/competitions/noaa-right-whale-recognition) | n\.a\. | 447 | North Atlantic right whale | 4544 ||
56 | | 2011 | [StripeSpotter](https://code.google.com/archive/p/stripespotter/downloads) | [Biometric animal databases from field photographs: identification of individual zebra in the wild](https://doi.org/10.1145/1991996.1992002) | 45 | plains and Grevy’s zebra | 820 ||
57 | | 2009 | [Whale Shark ID](https://lila.science/datasets/whale-shark-id) | [Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification](https://doi.org/10.3354/esr00186) | 543 | whale shark | 7693 ||
58 |
59 | Table Content Source (revised and additional content was added): [ Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification ](https://doi.org/10.3390/ani15010001)
60 |
61 |
62 | > [!TIP]
63 | > The [Wildlife Datasets Toolkit](https://github.com/WildlifeDatasets/wildlife-datasets) is an open-source toolkit for animal re-ID. It gathers publicly available animal re-ID datasets in one place in an effort to make them more easily accessible and to improve usability. Many of the above listed datasets have been added to the Toolkit.
64 | >
65 | > The [MegaDescriptor](https://huggingface.co/BVRA/MegaDescriptor-L-384) is a state-of-the-art model for animal re-ID and part of the Wildlife Datasets Toolkit. It was trained on several animal datasets and is species agnostic.
66 |
82 |
83 |
84 |
85 |
95 |
96 |
97 |
98 |
99 |
100 |
101 | ### Table 2: Further publications on the topic
102 | where the dataset could not be found and might not be public OR is only available on request
103 |
104 | |Year|Publication|Species|Identifying Feature|Note|
105 | |--------|--------|--------|--------|--------|
106 | |2025| [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://doi.org/10.1007/978-3-031-72649-1_2)| Cat, Chimp, Chinchilla, Degus, Dog, Ferret, Guinea, Hamster, Hedgehog, Parakeet, Java, Pig, Rabbit| face | d |
107 | |2024| [Transformer-Based Similarity Learning for Re-Identification of Chickens](http://dx.doi.org/10.2139/ssrn.4886408) |Chicken|head||
108 | |2023| [A Purely Visual Re-ID Approach for Bumblebees (Bombus terrestris)](https://doi.org/10.1016/j.atech.2022.100135) | Bumblebee| body||
109 | |2022| [Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision](https://jachansantiago.com/assets/pdf/VISAPP.pdf) |Honey Bees|abdomen||
110 | |2021| [YakReID-103: A Benchmark for Yak Re-Identification](https://doi.org/10.1109/IJCB52358.2021.9484341) |Yak|fur, texture, horns||
111 | |2021| [Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings](https://doi.org/10.1109/DICTA52665.2021.9647359) |Manta Rays|belly markings||
112 | |2021| [Dog Nose-Print Identification Using Deep Neural Networks](https://doi.org/10.1109/ACCESS.2021.3068517) |Dogs|nose||
113 | |2021| [Assessing the performance of open-source, semi-automated pattern recognition software for harbour seal (P. v. vitulina) photo ID](https://doi.org/10.1007/s42991-021-00165-8) |Harbour Seal|body pattern||
114 | |2020|[Bumblebee Re-Identification Dataset](https://doi.org/10.1109/WACVW50321.2020.9096909)|Bumblebee|body|b|
115 | |2020|[A study on giant panda recognition based on images of a large proportion of captive pandas](https://doi.org/10.1002/ece3.6152)|Giant Panda|face||
116 | |2020|[Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears](https://doi.org/10.1002/ece3.6840)|Brown Bear|face||
117 | |2020|[Image-based Individual Cow Recognition using Body Patterns](https://dx.doi.org/10.14569/IJACSA.2020.0110311)|4 Species of Cow|body pattern||
118 | |2019|[ELPephants: A Fine-Grained Dataset for Elephant Re-Identification](https://doi.org/10.1109/ICCVW.2019.00035)|Elephants|tusks, body shapes, injury marks|a|
119 | |2019|[Chimpanzee face recognition from videos in the wild using deep learning](https://doi.org/10.1126/sciadv.aaw0736)|Chimpanzee|face||
120 | |2019|[Distinguishing Individual Red Pandas from Their Faces](https://doi.org/10.1007/978-3-030-31723-2_61)|Red Panda|face||
121 | |2018|[Individual Common Dolphin Identification Via Metric Embedding Learning](https://doi.org/10.1109/IVCNZ.2018.8634778)|Common Dolphin|fin||
122 | |2018|[Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks](https://doi.org/10.4236/gep.2018.65003)|Dwarf Minke Whale|fin, scars, body||
123 | |2018|[Multi-views Embedding for Cattle Re-identification](https://doi.org/10.1109/SITIS.2018.00036)|Cattle|face||
124 | |2016|[Automated Visual Fin Identification of Individual Great White Sharks]( https://doi.org/10.1007/s11263-016-0961-y)|Great White Shark|fin|c|
125 | |2013|[An automated chimpanzee identification system using face detection and recognition](https://doi.org/10.1186/1687-5281-2013-49)|Chimpanzee|face||
126 |
127 |
128 |
129 |
130 |
131 |
132 | [a] can be accessed through the [Wildlife Datasets Toolkit](https://github.com/WildlifeDatasets/wildlife-datasets)
133 |
134 | [b] please refer to data@apic.ai for early access
135 |
136 | [c] to obtain a copy please directly contact: Michael C. Scholl, Save Our Seas Foundation (CEO), Michael@SaveOurSeas.com
137 |
138 | [d] available on request through this [form](https://forms.gle/xgkZSg6yWjfBhCGE6)
139 |
140 |
141 |
142 |
143 | ### More resources
144 | |Website|Dataset|Species|Identifying Feature|
145 | |--------|--------|--------|--------|
146 | |[Sarasota Dolphin Research Program](https://sarasotadolphin.org/)|[GoMDIS](https://sarasotadolphin.org/gomdis/)|bottlenose dolphins|dorsal fin|
147 | |[Cascadia Research Collective](https://cascadiaresearch.org/)|[Photo-ID Project](https://cascadiaresearch.org/project/photo-id/)|humpback and blue whale|flukes (humpback whale), dorsal fin (blue whale)|
148 | |[OBIS-SEAMAP](https://seamap.env.duke.edu/content/about)|[various datasets](https://seamap.env.duke.edu/dataset/list)|[various species](https://seamap.env.duke.edu/species/list)|-|
149 | |[Species 360](https://species360.org/)|[various data](https://conservation.species360.org/data-sharing/)|various species|-|
150 | |[Bay Cetology](https://baycetology.org/)|Bigg’s killer whale photo-ID data|killer whale|-|
151 |
152 |
153 | ### Further reading
154 | I'd like to highlight this publication from 2021 which gives a great overview of the entire topic
155 | [Perspectives on Individual Animal Identification from Biology and Computer Vision](https://doi.org/10.1093/icb/icab107)
156 |
157 |
158 | ## 🖋️ Citation
159 | Table 1: A collection of publicly available computer vision datasets for the re-identification of individual animals
160 | ```tex
161 | @Article{ani15010001,
162 | AUTHOR = {Kern, Daria and Schiele, Tobias and Klauck, Ulrich and Ingabire, Winfred},
163 | TITLE = {Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification},
164 | JOURNAL = {Animals},
165 | VOLUME = {15},
166 | YEAR = {2025},
167 | NUMBER = {1},
168 | ARTICLE-NUMBER = {1},
169 | URL = {https://www.mdpi.com/2076-2615/15/1/1},
170 | ISSN = {2076-2615},
171 | DOI = {10.3390/ani15010001}
172 | }
173 | ```
174 |
175 | Table 2: Further publications on the topic
176 | ```tex
177 | @misc{Kern_Individual_Animal_Re-ID_Datasets,
178 | author = {Daria Kern},
179 | title = {Individual Animal Re-ID Datasets},
180 | year = {2025},
181 | publisher = {GitHub},
182 | journal = {GitHub repository},
183 | howpublished = {\url{https://github.com/DariaKern/IndividualAnimalRe-IDDatasets}},
184 | }
185 | ```
186 |
187 |
190 |
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