| --- |
| license: cc-by-4.0 |
| viewer: false |
| annotations_creators: |
| - expert-generated |
| task_categories: |
| - object-detection |
| - other |
| language: |
| - en |
| pretty_name: PigBench |
| source_datasets: |
| - original |
| tags: |
| - object-detection |
| - multi-object-tracking |
| - pigs |
| - pig |
| - animals |
| - animal |
| - livestock |
| - computer-vision |
| - precision-livestock-farming |
| - image |
| - video |
| modalities: |
| - image |
| - video |
| doi: 10.1016/j.compag.2025.111264 |
| --- |
| |
|
|
| # 🐷 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](https://doi.org/10.1016/j.compag.2025.111264). |
|
|
| # ⬇️ Downloading the dataset |
|
|
| You can download the dataset using the Hugging Face CLI. First, install the following package: |
|
|
| ```bash |
| pip install huggingface_hub |
| ``` |
|
|
| The entire dataset (roughly 25 GB) can then be downloaded as follows: |
|
|
| ```bash |
| 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](https://doi.org/10.25625/I6UYE9) and [here](https://doi.org/10.25625/P7VQTP). The GitHub repository associated with this work can be found [here](https://github.com/jonaden94/PigBench). |
|
|
| 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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
