README.md

---
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