dilshadmohammed/mmla_wilds

Dataset Card for MMLA The Wilds

Dataset Details

This dataset contains annotated video frames of giraffes, Grevy's zebras, Persian onagers, and African Painted Dogs, collected at The Wilds in Ohio. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery. It includes frames from various sessions, with annotations indicating the presence of animals in the images in YOLO format, and is designed to facilitate research in wildlife monitoring and conservation using autonomous drones.

This dataset contains video frames collected at The Wilds Conservation Center in Ohio, USA, using drones. The Wilds is a 10,000 acre safari park and conservation center that is home to a variety of endangered species. The dataset includes video frames of African Painted Dogs, Giraffes, Persian Onangers, and Grevy's Zebras, captured during different sessions. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.

The dataset consists of frames. Each frame is accompanied by annotations in YOLO format, indicating the presence of animals and their bounding boxes within the images. The annotations were completed manually by the dataset curator using CVAT and kabr-tools.

Session	Date Collected	Size (pixels)	Total Frames	Species	Drone Model	Video IDs
session_1	2024-06-14	2720 x 1530	13,749	African Painted Dog	DJI Mini	DJI_0034, DJI_0035
session_2	2024-04-18	4096 x 2160	4,053	Persian Onanger	Parrot Anafi	P0100010, P0110011,P0080008, P0090009, P0070007, P0160016, P0120012
session_3	2024-07-31	3840 x 2160	3,436	Giraffe	Parrot Anafi	P0140018, P0150019
session_4	2024-07-31	4096 x 2160	506	Grevy's Zebra	Parrot Anafi	P0070010
Total Frames:			21,744

This table shows the data collected at The Wilds Conservation Center in Ohio, USA, with session information, dates, frame counts, and primary species observed.

The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.

See the fine-tuned YOLO11m model that was trained using this dataset.

Dataset Structure

/dataset/
    classes.txt
    session_1/
        DJI_0034/
            DJI_0034_000000.jpg
            DJI_0034_000000.txt
            ...
            DJI_0035_000013.txt
        DJI_0035/
            partition_1.zip
            partition_2.zip
            partition_3.zip
    session_2/
        P0140018/
          P0140018_000000.jpg
          P0140018_000000.txt
          ...
        P0150019/
          ...
          P0150019_000326.txt
    session_3/
        P0070007/
          P0070007_000000.jpg
          P0070007_000000.txt
          ...
        P0080008/
          ...
        P0090009/
          ...
        P0100010/
          ...
        P0110011/
          ...
        P0120012/
          ...
        P0160016/
          ...
          P0160016_000598.txt
    session_4/
        P0070010/
          P0070010_000000.jpg
          P0070010_000000.txt
          ...
          P0070010_000505.txt

Data Instances

All images are named <video_id>_<frame_number>.jpg, under the particular session and full video to which they belong; these can be matched to dates based on the table above. The annotations are in YOLO format and are stored in a corresponding .txt file with the same name as the image.
Note: the DJI_0035 files are in .zip folders, due to issues uploading the large directories to HF.

Data Fields

classes.txt:

• 0: zebra
• 1: giraffe
• 2: onager
• 3: dog

frame_id.txt:

• class: Class of the object in the image (0 for animal species)
• x_center: X coordinate of the center of the bounding box (normalized to [0, 1])
• y_center: Y coordinate of the center of the bounding box (normalized to [0, 1])
• width: Width of the bounding box (normalized to [0, 1])
• height: Height of the bounding box (normalized to [0, 1])

Data Splits

This dataset was used in conjunction with the other two MMLA datasets for both training and testing the MMLA YOLO model.

Dataset Creation

Curation Rationale

The dataset was created to facilitate research in wildlife monitoring and conservation using advanced imaging technologies. The goal is to develop and evaluate computer vision models that can accurately detect and classify animals from drone imagery, and their generalizability across different species and environments.

Source Data

Data Collection and Processing

The African Painted Dog missions were collected manually using a DJI Mavic Mini drone, while the Giraffe, Persian Onanger, and Grevy's Zebra missions were collected using a Parrot Anafi drone. The Grevy's zebras and giraffe missions were conducted semi-autonomously using the WildWing system, while the Persian onager data was collected manually. The drones were flown over the Wilds Conservation Center in Ohio, capturing video footage of the animals in their natural habitat.

The videos were annotated manually using the Computer Vision Annotation Tool (CVAT) and the kabr-tools library. These detection annotations and original video files were then processed to extract individual frames, which were saved as JPEG images. The annotations were converted to YOLO format, with bounding boxes indicating the presence of zebras in each frame.

Annotations

Annotation process

CVAT and kabr-tools were used to annotate the video frames. The annotation process involved manually labeling the presence of animals in each frame, drawing bounding boxes around them, and converting the annotations to YOLO format.

Who are the annotators?

Jenna Kline (The Ohio State University) - ORCID: 0009-0006-7301-5774
Alison Zhong (The Ohio State University)
Jake Yablok (The Ohio State University)

Personal and Sensitive Information

The dataset was cleaned to remove any personal or sensitive information.

Licensing Information

This dataset is dedicated to the public domain (by applying the CC0-1.0 Public Domain Waiver) for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research.

Citation

BibTeX:

Data

@misc{mmla_wilds,
  author = {  Jenna Kline,
              Alison Zhong,
              Jake Yablok
          },
  title = {MMLA The Wilds Dataset (Revision e61014d)},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/mmla-wilds},
  doi = {10.57967/hf/7379},
  publisher = {Hugging Face}
}

Paper

@misc{kline2025mmla,
      title={MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset}, 
      author={Jenna Kline and Samuel Stevens and Guy Maalouf and Camille Rondeau Saint-Jean and Dat Nguyen Ngoc and Majid Mirmehdi and David Guerin and Tilo Burghardt and Elzbieta Pastucha and Blair Costelloe and Matthew Watson and Thomas Richardson and Ulrik Pagh Schultz Lundquist},
      year={2025},
      eprint={2504.07744},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.07744}, 
}

If you use this dataset, please also cite the WildWing video dataset used to generate data for sessions 2-4.

Related Papers

@article{kline2025wildwing,
  title={WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring},
  author={Kline, Jenna and Zhong, Alison and Irizarry, Kevyn and Stewart, Charles V and Stewart, Christopher and Rubenstein, Daniel I and Berger-Wolf, Tanya},
  journal={Methods in Ecology and Evolution},
  year={2025},
  doi={10.1111/2041-210X.70018},
  publisher={Wiley Online Library}
}

Acknowledgements

This work was supported by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This work was supported by the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment ICICLE, which is funded by the US National Science Foundation under grant number OAC-2112606.

More Information

The data was gathered at The Wilds with permission from The Wilds Science Committee to take field observations and fly drones in the pastures.

Dataset Card Authors

Jenna Kline

Dataset Card Contact

kline.377 at osu.edu