README.md

---
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- config_name: reidentification
  data_files:
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    path: reidentification/train-*
  - split: test
    path: reidentification/test-*
license: cc-by-4.0
---

# Wolverine re-identification from camera trap imagery

Images and labels for a manuscript under review at *Ecological Informatics* on
automated re-identification and novelty detection of wolverines (*Gulo gulo*)
from bait-station camera trap images. Code, runbooks, and results are in the
GitHub repository [mosscoder/mpg-wolverines](https://github.com/mosscoder/mpg-wolverines).

## Project Overview

Wolverines are listed as threatened under the U.S. Endangered Species Act and
occur at low density in remote terrain, so camera trap re-identification is
one of few practical ways to study them. A bait frame exposes the ventral
pelage pattern to a nearby game camera, but the animal moves while it feeds and
the pattern is clearly visible in only a fraction of images. The pipeline has
two stages.

1. **Pelage visibility classifier.** An image classifier scores every image
   from 0 to 1, the estimated probability that the pelage pattern is clearly
   visible. We call this the quality score.
2. **Re-identification with novelty detection.** A frozen image encoder turns
   each image into an encoder output, a trained projection head maps that
   output to an embedding, and images are compared by the cosine similarity of
   their embeddings. A query is assigned to the known individual of its
   nearest gallery image, or flagged as unknown when that similarity falls
   below a threshold. We compared three encoders: DINOv3-ViT-B/16
   (general-purpose; [Siméoni et al. 2025](https://doi.org/10.48550/arXiv.2508.10104)),
   BioCLIP-2 ViT-L/14 (biology-specific; [Gu et al. 2025](https://doi.org/10.48550/arXiv.2505.23883)),
   and MegaDescriptor-L-384 (wildlife-specific; [Čermák et al. 2023](https://doi.org/10.48550/arXiv.2311.09118)).

Quality score thresholds of 0, 0.25, and 0.50 are applied to the gallery, to
the queries, or to both. Re-identification is scored by recall at rank one and
novelty detection by balanced accuracy, each averaged over individuals. There
are two experiments, a few-shot experiment (2 to 64 gallery images per
individual, eight seeds) and a full-data experiment that trains on every
eligible gallery image. Both report performance on the test split, the most
recent events of each known individual, at checkpoints selected on the
validation queries.

![Ten images from one camera trap event for each of three individuals, sorted left to right by quality score](assets/quality_gradient.png)

*The quality score as a gradient within a single camera trap event. Each row is
one daytime event of one individual, ten images sorted from low to high score,
with the score printed on each image. Camera, scene, and lighting are fixed
within a row, so the score changes with the animal's pose alone.*

## Configurations

Every image is a MegaDetector crop of one wolverine. Splits are temporal within
each individual, so no test image predates a training image of the same
animal. The dataset carries no coordinates.

**`pelage`** holds the human-labeled crops used to train the pelage visibility
classifier, 2,431 training and 270 test images from the earliest camera trap
events of each individual. `label` is the annotator's three-level rating of
the ventral pelage pattern (0 None, 1 Partial, 2 Full), and the classifier
treats Full as visible and the other two as not visible. Other columns give the
individual (`id`), the event start as YYYYMMDDHHMM (`ymdh`), color or
black-and-white infrared capture (`color`, 1 or 0), the source filename, and
the MegaDetector confidence and bounding box.

**`reidentification`** holds every scored crop from the remaining events,
44,201 training and 5,111 test images from 11 individuals, with the
classifier's `pelage_score` (the quality score) and the individual's identity.
The test split is the most recent 10% of each individual's events. Which
individuals serve as known individuals, which as unknown individuals, and which
events become validation queries is decided downstream, as documented in the
repository's
[role assignment runbook](https://github.com/mosscoder/mpg-wolverines/blob/main/preprocessing/README.md).

```python
from datasets import load_dataset
ds = load_dataset("mpg-ranch/wolverines", "reidentification", split="train")
```

How the dataset was built from raw camera trap images is documented in the
repository's
[dataset creation runbook](https://github.com/mosscoder/mpg-wolverines/blob/main/hugging_face_dataset/v2/README.md).

## Runbooks

Each pipeline stage has a README in the GitHub repository that lists its
scripts in run order, their inputs and outputs, and the values used for the
manuscript.

1. [Dataset creation](https://github.com/mosscoder/mpg-wolverines/blob/main/hugging_face_dataset/v2/README.md)
2. [Pelage visibility classifier](https://github.com/mosscoder/mpg-wolverines/blob/main/pelage_sorting/README.md)
3. [Role assignment and manuscript assets](https://github.com/mosscoder/mpg-wolverines/blob/main/preprocessing/README.md)
4. [Re-identification and novelty detection](https://github.com/mosscoder/mpg-wolverines/blob/main/reid_openset/README.md)

## Citation

Cite the dataset as:

Doherty, K., Baughan, K., Davis, B., and Ramsey, P. 2026. wolverines.
Hugging Face. https://doi.org/10.57967/hf/10575

```bibtex
@misc{kyle_doherty_2026,
    author    = { Kyle Doherty and Kalon Baughan and Bret Davis and Philip Ramsey },
    title     = { wolverines (Revision 5316a09) },
    year      = 2026,
    url       = { https://huggingface.co/datasets/mpg-ranch/wolverines },
    doi       = { 10.57967/hf/10575 },
    publisher = { Hugging Face }
}
```

The manuscript citation will be added on publication.

## References

- Čermák, V., Picek, L., Adam, L., and Papafitsoros, K. 2023. WildlifeDatasets: an
  open-source toolkit for animal re-identification. arXiv:2311.09118.
  https://doi.org/10.48550/arXiv.2311.09118
- Gu, J., Stevens, S., Campolongo, E. G., Thompson, M. J., Zhang, N., Wu, J.,
  Kopanev, A., Mai, Z., White, A. E., Balhoff, J., Dahdul, W., Rubenstein, D.,
  Lapp, H., Berger-Wolf, T., Chao, W.-L., and Su, Y. 2025. BioCLIP 2: emergent
  properties from scaling hierarchical contrastive learning. arXiv:2505.23883.
  https://doi.org/10.48550/arXiv.2505.23883
- Hernandez, A., Miao, Z., Vargas, L., Beery, S., Dodhia, R., Arbelaez, P., and
  Lavista Ferres, J. M. 2024. Pytorch-Wildlife: a collaborative deep learning
  framework for conservation. arXiv:2405.12930.
  https://doi.org/10.48550/arXiv.2405.12930
- Siméoni, O., Vo, H. V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C.,
  Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D.,
  Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C.,
  Vedaldi, A., Tolan, J., Brandt, J., Couprie, C., Mairal, J., Jégou, H.,
  Labatut, P., and Bojanowski, P. 2025. DINOv3. arXiv:2508.10104.
  https://doi.org/10.48550/arXiv.2508.10104