jonaden/pig-detection-and-tracking

🐷 Dataset for the paper: Benchmarking pig detection and tracking under diverse and challenging conditions

Note: This dataset does not provide a load_dataset interface.

More detailed descriptions about the dataset can be found in the corresponding paper.

⬇️ Downloading the dataset

You can download the dataset using the Hugging Face CLI. First, install the following package:

pip install huggingface_hub

The entire dataset (roughly 25 GB) can then be downloaded as follows:

hf download \
  jonaden/pig-detection-and-tracking \
  --repo-type dataset \
  --local-dir ./pig-detection-and-tracking

You can also either specify an individual file to download or use the --include flag to download a specific folder.

🗂️ Overview

The data is structured as follows:

pig-detection-and-tracking/
├── detection/
  ├── PigDetect.zip
  └── coco_annotations/
    ├── train.json
    ├── val.json
    └── test.json
└── tracking/
  ├── PigTrack/
    ├── pigtrack0001.zip
    ├── pigtrack0002.zip
    ├── ...
    ├── pigtrack0080.zip
    └── split.txt
  ├── PigTrackVideos.zip
  └── PigTrackViz.zip

PigDetect.zip contains 2931 jpeg images from 31 different conventional barn environments. The ccoo_annotations folder contains the bounding box annotations.

Regarding the tracking dataset, each sequence pigtrackxxxx.zip (80 in total, spanning roughly 40 minutes of video footage from 9 different barn environments) contains the corresponding jpeg images and multi-object tracking annotations in the DanceTrack format.

PigTrackVideos.zip and PigTrackViz.zip provide the raw mp4 videos of the sequences as well as the visualized annotations.

More information about all files can be found in the READMEs of the original data repositories located here and here. The GitHub repository associated with this work can be found here.

Further files related to the work, such as trained model checkpoints for pig detection and tracking, can also be found there.

📌 Citation

If you find this dataset useful, please cite as follows:

@article{pigbench2026,
  title     = {Benchmarking pig detection and tracking under diverse and challenging conditions},
  author    = {Henrich, Jonathan and Post, Christian and Zilke, Maximilian and Shiroya, Parth and Chanut, Emma and Yamchi, Amir Mollazadeh and Yahyapour, Ramin and Kneib, Thomas and Traulsen, Imke},
  journal   = {Computers and Electronics in Agriculture},
  volume    = {241},
  pages     = {111264},
  year      = {2026},
  publisher = {Elsevier},
  doi       = {10.1016/j.compag.2025.111264}
}