gorilla-watch/Gorilla-Zoo-Berlin

Gorilla-Berlin-Zoo Dataset

Overview

The Gorilla-Berlin-Zoo dataset serves as a cross-domain evaluation benchmark dataset for gorilla re-identification systems, offering camera trap footage of Western Lowland Gorillas (Gorilla gorilla gorilla) in a controlled zoo environment.

The dataset is part of the **GorillaWatch** project, which introduces an end-to-end pipeline integrating detection, tracking, and re-identification for automated gorilla monitoring. Additionally, we also offer a large-scale in-the-wild dataset **Gorilla-SPAC-Wild**.

Dataset Description

Key Features

• —Large-scale Evaluation Dataset: Videos extracted from camera trap footage at Berlin Zoo
• —Controlled Environment: Consistent camera positions and controlled zoo environment with focus on a single social group
• —Domain Shift: New setting with unique features compared to the rainforest (artificial structures, glass, different camera angles)
• —Generalization Evaluation: Enables the evaluation of generalization capabilities of gorilla re-identification models trained on in-the-wild data.

Example Images

[image] [image] [image] [image] [image] [image] These images show some examples of the same dataset entry in the different dataset configs. The large one is the full frame (content of the image column in the two fullimage configs) with the corresponding bounding boxes for face and body drawn onto it. Next to it are the face and body crops, which can be found in the image and/or bodyimage columns in the body and facewithbody configs.

Data Extraction & Processing

• —Images are extracted from automatically generated trackings using a YOLOv8-Nano model, which has been finetuned for gorilla detection, and a BoostTrack++ tracker
• —Ground truth labels provided by the gorillas' caretakers with years of experience with this population
• —For cropping, the bounding boxes were squared to avoid distortion of important facial features in the preprocessing's resizing

Dataset Split

This dataset only contains a single test split, as its primary intended use case is to evaluate in-the-wild-focused models on a controlled population. Additionally, to follow the open set approach for training and evaluation sets, this population is simply too small.

Use Cases

1. 1.Cross-domain evaluation: Test generalization from in-the-wild to controlled environments
2. 2.Face-body analysis: Paired crops enable multi-modal re-identification research
3. 3.Tracking evaluation: Dense annotations support multi-object tracking benchmarks
4. 4.Behavioral analysis: Controlled setting enables study of social interactions
5. 5.Domain adaptation: Bridge gap between field and captive populations

Dataset Schema

Across all configs, each sample includes:

• —image: Primary image (depending on config, encoded as image bytes)
• —class: Individual gorilla name (Bibi, Tilla, Djambala, Sango, M'Penzi)
• —date: Capture date (YYYY-MM-DD format)
• —time: Capture time (HH:mm:ss)
• —video: Source video identifier
• —frame_number: Frame number within the video
• —camera: Camera trap identifier (zoo1, zoo2, zoo3, zoo4)

Depending on the config, there are slight differences in the available columns and their contents:

• —body:
• —image: Body crop
• —face_with_body:
• —image: Face crop
• —body_image: Corresponding body crop. (The intention behind this is, to have subset of the body dataset, where faces are visible.)
• —full_image_bbox_body:
• —image: Full video frame
• —bbox: Detected body bounding box (in the format [xmin,ymin, width, height])
• —full_image_bbox_face_with_body:
• —image: Full video frame
• —bbox: Detected face bounding box
• —body_bbox: Detected body bounding box

Note that the full_image configs do not contain all detection entries for a frame in a single entry. If there are multiple gorillas detected and annotated in the same frame, there will be multiple dataset entries for that frame, each containing one gorilla. There can also be frames where not all visible gorillas are annotated, due to faulty detection and tracking.

Dataset Statistics

• —Total samples: 188,679 face bounding boxes [image]
• —Total Videos: 153 different source videos
• —Tracklets: 275 distinct tracking sequences
• —Individuals: 5 gorillas [image]
• —Cameras: 4 different viewpoints [image]
• —Duration: 3 months of recording [image]
• —Environment: Controlled zoo setting

Source Videos

Additionally to the prepared dataset, we provide the source videos from which the images are taken. They are provided in the videos.tar.gz archive in this repo and can be unpacked with tar -xzf videos.tar.gz.

Loading and Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("gorilla-watch/Gorilla-Zoo-Berlin", "face_with_body")

test_data = dataset["test"]

# Access samples
sample = test_data[0]
face_image = sample["image"]  # PIL Image object
gorilla_id = sample["class"]   # Individual identifier

Performance Benchmarks

From our paper:

Note: Ensemble methods significantly outperform end-to-end video architectures on this dataset.

License

This dataset is released under the CC-BY-4.0 License.

Citation

If you use this dataset in academic work, please cite the original GorillaWatch paper:

@inproceedings{GorillaWatch2026,
  title={GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring}, 
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  author={Maximilian Schall and Felix Leonard Knöfel and Noah Elias König and Jan Jonas Kubeler and Maximilian von Klinski and Joan Wilhelm Linnemann and Xiaoshi Liu and Iven Jelle Schlegelmilch and Ole Woyciniuk and Alexandra Schild and Dante Wasmuht and Magdalena Bermejo Espinet and German Illera Basas and Gerard de Melo},
  year={2026},
  archivePrefix={arXiv},
  eprint={2512.07776}
}

Acknowledgments

This project was funded by the Federal Ministry of Research, Technology and Space under the funding code “KI-Servicezentrum Berlin-Brandenburg” 16IS22092. We are grateful to Zoo Berlin for their expert assistance and facility access. This collaboration enabled the development of AI tools capable of being deployed in the wild to directly support gorilla conservation. The responsibility for the content of this publication remains with the authors.