Datasets:
Formats:
imagefolder
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K - 10K
License:
File size: 2,209 Bytes
fa7a4f7 5abd0c4 fa7a4f7 5abd0c4 fa7a4f7 7459b61 fa7a4f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
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:
## 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]
|