| --- |
| dataset_info: |
| - config_name: pelage |
| features: |
| - name: id |
| dtype: string |
| - name: ymdh |
| dtype: int64 |
| - name: color |
| dtype: int32 |
| - name: label |
| dtype: int32 |
| - name: filename |
| dtype: string |
| - name: confidence |
| dtype: float32 |
| - name: bbox_x |
| dtype: float32 |
| - name: bbox_y |
| dtype: float32 |
| - name: bbox_width |
| dtype: float32 |
| - name: bbox_height |
| dtype: float32 |
| - name: bbox_area |
| dtype: float32 |
| - name: image |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 341869702.47 |
| num_examples: 2431 |
| - name: test |
| num_bytes: 39377260.0 |
| num_examples: 270 |
| download_size: 384901548 |
| dataset_size: 381246962.47 |
| - config_name: reidentification |
| features: |
| - name: id |
| dtype: string |
| - name: ymdh |
| dtype: int64 |
| - name: color |
| dtype: int32 |
| - name: pelage_score |
| dtype: float32 |
| - name: data_source |
| dtype: string |
| - name: filename |
| dtype: string |
| - name: megadetector_confidence |
| dtype: float32 |
| - name: bbox_x |
| dtype: float32 |
| - name: bbox_y |
| dtype: float32 |
| - name: bbox_width |
| dtype: float32 |
| - name: bbox_height |
| dtype: float32 |
| - name: bbox_area |
| dtype: float32 |
| - name: image |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 3618210586.233 |
| num_examples: 44201 |
| - name: test |
| num_bytes: 1029302323.644 |
| num_examples: 5111 |
| download_size: 8016418733 |
| dataset_size: 4647512909.877 |
| configs: |
| - config_name: pelage |
| data_files: |
| - split: train |
| path: pelage/train-* |
| - split: test |
| path: pelage/test-* |
| - config_name: reidentification |
| data_files: |
| - split: train |
| 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. |
|
|
|  |
|
|
| *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 |
|
|
