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license: cc-by-4.0
task_categories:
- keypoint-detection
- image-classification
tags:
- cattle
- thermal-imaging
- livestock
- animal-welfare
- fever-detection
- precision-agriculture
- RGB-Thermal
- facial-landmarks
- temperature-estimation
size_categories:
- 1K<n<10K
language:
- en
pretty_name: CattleFace-RGBT
---
# CattleFace-RGBT: Cattle Facial Landmark Dataset with RGB-Thermal Imagery
## Dataset Description
CattleFace-RGBT is the first publicly available multimodal dataset featuring paired frontal-view RGB and thermal facial images of cattle, annotated with 13 facial keypoints and associated ground-truth rectal temperature measurements. The dataset was developed to support research in automated cattle fever estimation and precision livestock farming.
**Paper:** [CattleFever: An automated cattle fever estimation system](https://doi.org/10.1016/j.atech.2025.101434)
**Published in:** Smart Agricultural Technology, Volume 12, 2025
## Dataset Summary
| Component | Count |
|-----------|-------|
| RGB images (annotated) | 1,890 |
| Thermal JPG images (annotated, colorized) | 2,611 |
| Raw thermal TIFF frames | 30,954 |
| Thermal videos (.mp4) | 51 |
| Unique cattle | 108 |
| Cattle with temperature readings | 29 |
| Facial keypoints per image | 13 |
| Recording dates | 3 (Feb 1, Feb 6, Feb 13) |
## Dataset Structure
```
CattleFace-RGBT/
├── README.md
├── rgb/ # RGB images organized by folder
│ ├── 1/
│ ├── 17/
│ ├── 25/
│ ├── 50/
│ └── 64/
├── thermal/ # Colorized thermal JPG images
│ ├── 1/
│ ├── 2/
│ ├── 17/
│ ├── 25/
│ ├── 50/
│ └── 64/
├── thermal_raw/ # Raw thermal TIFF frames (temperature data)
│ ├── 02_01/ # Feb 1 recording session
│ ├── 02_06/ # Feb 6 recording session
│ └── 02_13/ # Feb 13 recording session
└── annotations/
├── rgb_keypoints.json # COCO-format keypoint annotations for RGB
├── thermal_keypoints.json # COCO-format keypoint annotations for thermal
├── metadata.csv # Cow ID, temperature, and data mapping
└── cow_mapping.json # Sequence number → cow tag ID mapping
```
## Annotation Format
Annotations follow the COCO keypoint format:
### Images
```json
{
"id": 0,
"file_name": "rgb/1/00001.jpg",
"width": 2560,
"height": 1440,
"folder": "1",
"frame_id": "00001"
}
```
### Annotations
```json
{
"id": 0,
"image_id": 0,
"category_id": 1,
"keypoints": [x1, y1, v1, x2, y2, v2, ...],
"num_keypoints": 13,
"bbox": [x, y, width, height],
"area": 123456,
"iscrowd": 0
}
```
### 13 Facial Keypoints
| Index | Name | Description |
|-------|------|-------------|
| 1 | left_ear_base | Base of left ear |
| 2 | left_ear_middle | Middle of left ear |
| 3 | left_ear_tip | Tip of left ear |
| 4 | poll | Top of head (poll) |
| 5 | right_ear_base | Base of right ear |
| 6 | right_ear_middle | Middle of right ear |
| 7 | right_ear_tip | Tip of right ear |
| 8 | left_eye | Left eye |
| 9 | right_eye | Right eye |
| 10 | muzzle | Center of muzzle |
| 11 | left_nostril | Left nostril |
| 12 | right_nostril | Right nostril |
| 13 | mouth | Mouth |
Visibility flag: `0` = not labeled, `2` = labeled and visible.
## Raw Thermal Data
The `thermal_raw/` directory contains raw TIFF frames from the ICI FMX 400 thermal camera (384 x 288 pixels). Each pixel contains a temperature value in Celsius. These files can be read with:
```python
from PIL import Image
import numpy as np
tiff = Image.open("thermal_raw/02_01/0001_Video_Frame_1.tiff")
temp_array = np.array(tiff) # Temperature values in Celsius
```
TIFF filenames follow the pattern: `{sequence_num}_Video_Frame_{frame_num}.tiff`
Use `cow_mapping.json` to map sequence numbers to cow tag IDs and temperatures.
## Temperature Data
Ground-truth rectal temperatures (in Fahrenheit) are available for 29 cattle across 3 recording sessions. The mapping is provided in `metadata.csv` and `cow_mapping.json`.
## Data Collection
Data was collected at the Arkansas Agricultural Experiment Station, Savoy Research Complex, Beef Cattle Research Area, in partnership with the University of Arkansas. The setup used:
- **RGB camera:** Standard webcam (2560 x 1440 resolution)
- **Thermal camera:** ICI FMX 400 (384 x 288 pixel resolution, 50 Hz frame rate, < 0.03°C thermal sensitivity)
- **Temperature:** Rectal thermometer (ground truth)
Each calf was guided into a cattle squeeze chute for ~20 seconds of synchronized RGB and thermal video recording.
## Supported Tasks
1. **Cattle facial landmark detection** — Detect 13 keypoints on cattle faces
2. **Cattle face detection** — Detect and localize cattle faces using bounding boxes
3. **Core body temperature estimation** — Predict rectal temperature from thermal facial features
## Recommended Splits
As described in the paper:
- **Keypoint detection:** 70% train / 30% test (random split)
- **Temperature estimation:** 80% train / 20% test
## Citation
```bibtex
@article{pham2025cattlefever,
title={CattleFever: An automated cattle fever estimation system},
author={Pham, Trong Thang and Coffman, Ethan and Kegley, Beth and Powell, Jeremy G. and Zhao, Jiangchao and Le, Ngan},
journal={Smart Agricultural Technology},
volume={12},
pages={101434},
year={2025},
publisher={Elsevier},
doi={10.1016/j.atech.2025.101434}
}
```
## License
This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
## Contact
For questions about this dataset, please contact:
- Trong Thang Pham (tp030@uark.edu) — AICV Lab, University of Arkansas
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