README.md

---
annotations_creators:
  - expert-annotated
language:
  - en
license: other
multilinguality: monolingual
dataset_name: Gilt Posture Dataset
task_categories:
  - object-detection
  - image-classification
task_ids:
  - multi-class-image-classification
tags:
  - animal-behavior
  - pigs
  - rgb-d
  - depth-sensing
  - yolo
  - posture
---

# Gilt Posture Recognition Dataset

- Each RGB image has a matching depth image (same filename, `.png` extension).
- YOLO-format label files correspond to each image.

## 🐷 Annotated Postures

Five postures are labeled using YOLO bounding boxes:

| Class Name       | Class ID |
|------------------|----------|
| feeding          | 0        |
| lateral_lying    | 1        |
| sitting          | 2        |
| standing         | 3        |
| sternal_lying    | 4        |

## 📊 Class Distribution

Below is a histogram showing the distribution of posture classes across the dataset:

![Class Histogram](assets/class_histogram.png)

## Dataset Description

The total dataset is split randomly into training, validation, and testing sets (0.75:0.15:0.1). The filename of each image and corresponding labels are assigned with date and time of image captured prefixed by pen and camera identity (p1c1_20250108_080409.png == image of pen1 camera1  captured on January 08, 2025 at 08:04:09 o'clock)

- The Color folder contains the color images and corresponding labels. 
- Depth folder contains the height information of scene from the floor in mm unit and saved as uint16 format. 
- RGBD folder contains the combined pairs of color and depth images. The normalized height information is added as 4th channel (RGBA).
- Each folder contains a labels folder for the corresponding labeling information

## 🧠 Use Cases

- Animal behavior monitoring
- Multimodal object detection (RGB + Depth)
- Precision livestock farming

## License

The author has granted permission to download, use and redistribute this dataset only for research purposes. 

## Citation

Please cite as Bhujel A. et al. (2025). A Computer Vision dataset for Gilts' daily activity monitoring and tracking.  

## Contact

For questions or collaborations, feel free to reach out at [email protected]