Initial upload: CattleFace-RGBT dataset with corrected annotations

README.md

@@ -0,0 +1,188 @@
+---
+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

annotations/cow_mapping.json

@@ -0,0 +1,440 @@
+{
+  "02_01": {
+    "0001": {
+      "cow_tag": "1103",
+      "temperature_f": 103.1
+    },
+    "0002": {
+      "cow_tag": "1099",
+      "temperature_f": 105.0
+    },
+    "0006": {
+      "cow_tag": "1061",
+      "temperature_f": 107.4
+    },
+    "0007": {
+      "cow_tag": "1035",
+      "temperature_f": 107.1
+    },
+    "0008": {
+      "cow_tag": "1032",
+      "temperature_f": 103.4
+    },
+    "0009": {
+      "cow_tag": "1081",
+      "temperature_f": 104.1
+    },
+    "0010": {
+      "cow_tag": "1078",
+      "temperature_f": 105.1
+    },
+    "0011": {
+      "cow_tag": "1063",
+      "temperature_f": 107.1
+    },
+    "0012": {
+      "cow_tag": "1065",
+      "temperature_f": 104.1
+    },
+    "0013": {
+      "cow_tag": "1092",
+      "temperature_f": 102.3
+    }
+  },
+  "02_06": {
+    "0001": {
+      "cow_tag": "1078",
+      "temperature_f": 105.2
+    },
+    "0002": {
+      "cow_tag": "1125",
+      "temperature_f": 106.6
+    }
+  },
+  "02_13": {
+    "0001": {
+      "cow_tag": "1090",
+      "temperature_f": null
+    },
+    "0002": {
+      "cow_tag": "1115",
+      "temperature_f": 103.9
+    },
+    "0003": {
+      "cow_tag": "1119",
+      "temperature_f": null
+    },
+    "0004": {
+      "cow_tag": "1080",
+      "temperature_f": 102.6
+    },
+    "0005": {
+      "cow_tag": "1086",
+      "temperature_f": null
+    },
+    "0006": {
+      "cow_tag": "1070",
+      "temperature_f": null
+    },
+    "0007": {
+      "cow_tag": "1101",
+      "temperature_f": null
+    },
+    "0008": {
+      "cow_tag": "1053",
+      "temperature_f": null
+    },
+    "0009": {
+      "cow_tag": "1062",
+      "temperature_f": null
+    },
+    "0010": {
+      "cow_tag": "1056",
+      "temperature_f": null
+    },
+    "0011": {
+      "cow_tag": "1123",
+      "temperature_f": null
+    },
+    "0012": {
+      "cow_tag": "1055",
+      "temperature_f": null
+    },
+    "0013": {
+      "cow_tag": "1124",
+      "temperature_f": null
+    },
+    "0014": {
+      "cow_tag": "1095",
+      "temperature_f": 103.0
+    },
+    "0015": {
+      "cow_tag": "1106",
+      "temperature_f": null
+    },
+    "0016": {
+      "cow_tag": "1121",
+      "temperature_f": null
+    },
+    "0017": {
+      "cow_tag": "1063",
+      "temperature_f": null
+    },
+    "0018": {
+      "cow_tag": "1078",
+      "temperature_f": 103.9
+    },
+    "0019": {
+      "cow_tag": "1110",
+      "temperature_f": null
+    },
+    "0020": {
+      "cow_tag": "1108",
+      "temperature_f": null
+    },
+    "0021": {
+      "cow_tag": "1127",
+      "temperature_f": null
+    },
+    "0022": {
+      "cow_tag": "1096",
+      "temperature_f": null
+    },
+    "0023": {
+      "cow_tag": "1091",
+      "temperature_f": 103.0
+    },
+    "0024": {
+      "cow_tag": "1045",
+      "temperature_f": 104.7
+    },
+    "0025": {
+      "cow_tag": "1087",
+      "temperature_f": null
+    },
+    "0026": {
+      "cow_tag": "1113",
+      "temperature_f": null
+    },
+    "0027": {
+      "cow_tag": "1120",
+      "temperature_f": 104.1
+    },
+    "0028": {
+      "cow_tag": "1049",
+      "temperature_f": null
+    },
+    "0029": {
+      "cow_tag": "1117",
+      "temperature_f": null
+    },
+    "0030": {
+      "cow_tag": "1064",
+      "temperature_f": null
+    },
+    "0031": {
+      "cow_tag": "1043",
+      "temperature_f": null
+    },
+    "0032": {
+      "cow_tag": "1125",
+      "temperature_f": 103.8
+    },
+    "0033": {
+      "cow_tag": "1037",
+      "temperature_f": null
+    },
+    "0034": {
+      "cow_tag": "1048",
+      "temperature_f": null
+    },
+    "0035": {
+      "cow_tag": "1097",
+      "temperature_f": 102.5
+    },
+    "0036": {
+      "cow_tag": "1047",
+      "temperature_f": null
+    },
+    "0037": {
+      "cow_tag": "1082",
+      "temperature_f": 102.4
+    },
+    "0038": {
+      "cow_tag": "1100",
+      "temperature_f": null
+    },
+    "0039": {
+      "cow_tag": "1040",
+      "temperature_f": null
+    },
+    "0040": {
+      "cow_tag": "1116",
+      "temperature_f": null
+    },
+    "0041": {
+      "cow_tag": "1105",
+      "temperature_f": null
+    },
+    "0042": {
+      "cow_tag": "1085",
+      "temperature_f": 103.1
+    },
+    "0043": {
+      "cow_tag": "1084",
+      "temperature_f": null
+    },
+    "0044": {
+      "cow_tag": "1112",
+      "temperature_f": null
+    },
+    "0045": {
+      "cow_tag": "1058",
+      "temperature_f": null
+    },
+    "0046": {
+      "cow_tag": "1031",
+      "temperature_f": null
+    },
+    "0047": {
+      "cow_tag": "1083",
+      "temperature_f": null
+    },
+    "0048": {
+      "cow_tag": "1089",
+      "temperature_f": 104.2
+    },
+    "0049": {
+      "cow_tag": "1069",
+      "temperature_f": null
+    },
+    "0050": {
+      "cow_tag": "1036",
+      "temperature_f": null
+    },
+    "0051": {
+      "cow_tag": "1122",
+      "temperature_f": null
+    },
+    "0052": {
+      "cow_tag": "1107",
+      "temperature_f": null
+    },
+    "0053": {
+      "cow_tag": "1118",
+      "temperature_f": null
+    },
+    "0054": {
+      "cow_tag": "1094",
+      "temperature_f": 103.4
+    },
+    "0055": {
+      "cow_tag": "1034",
+      "temperature_f": null
+    },
+    "0056": {
+      "cow_tag": "1077",
+      "temperature_f": null
+    },
+    "0057": {
+      "cow_tag": "1092",
+      "temperature_f": null
+    },
+    "0058": {
+      "cow_tag": "1074",
+      "temperature_f": null
+    },
+    "0059": {
+      "cow_tag": "1054",
+      "temperature_f": null
+    },
+    "0060": {
+      "cow_tag": "1072",
+      "temperature_f": null
+    },
+    "0061": {
+      "cow_tag": "1111",
+      "temperature_f": null
+    },
+    "0062": {
+      "cow_tag": "1109",
+      "temperature_f": null
+    },
+    "0063": {
+      "cow_tag": "1042",
+      "temperature_f": null
+    },
+    "0064": {
+      "cow_tag": "1059",
+      "temperature_f": null
+    },
+    "0065": {
+      "cow_tag": "1067",
+      "temperature_f": null
+    },
+    "0066": {
+      "cow_tag": "1088",
+      "temperature_f": null
+    },
+    "0067": {
+      "cow_tag": "1066",
+      "temperature_f": null
+    },
+    "0068": {
+      "cow_tag": "1032",
+      "temperature_f": null
+    },
+    "0069": {
+      "cow_tag": "1098",
+      "temperature_f": null
+    },
+    "0070": {
+      "cow_tag": "1035",
+      "temperature_f": null
+    },
+    "0071": {
+      "cow_tag": "1046",
+      "temperature_f": null
+    },
+    "0072": {
+      "cow_tag": "1081",
+      "temperature_f": null
+    },
+    "0073": {
+      "cow_tag": "1039",
+      "temperature_f": null
+    },
+    "0074": {
+      "cow_tag": "1114",
+      "temperature_f": null
+    },
+    "0075": {
+      "cow_tag": "1102",
+      "temperature_f": null
+    },
+    "0076": {
+      "cow_tag": "1075",
+      "temperature_f": null
+    },
+    "0077": {
+      "cow_tag": "1038",
+      "temperature_f": null
+    },
+    "0078": {
+      "cow_tag": "1052",
+      "temperature_f": null
+    },
+    "0079": {
+      "cow_tag": "1060",
+      "temperature_f": null
+    },
+    "0080": {
+      "cow_tag": "1099",
+      "temperature_f": null
+    },
+    "0082": {
+      "cow_tag": "1061",
+      "temperature_f": null
+    },
+    "0083": {
+      "cow_tag": "1050",
+      "temperature_f": null
+    },
+    "0084": {
+      "cow_tag": "1079",
+      "temperature_f": null
+    },
+    "0085": {
+      "cow_tag": "1103",
+      "temperature_f": null
+    },
+    "0086": {
+      "cow_tag": "1104",
+      "temperature_f": 103.7
+    },
+    "0087": {
+      "cow_tag": "1068",
+      "temperature_f": null
+    },
+    "0088": {
+      "cow_tag": "1071",
+      "temperature_f": null
+    },
+    "0089": {
+      "cow_tag": "1051",
+      "temperature_f": null
+    },
+    "0090": {
+      "cow_tag": "1057",
+      "temperature_f": 102.7
+    },
+    "0091": {
+      "cow_tag": "1076",
+      "temperature_f": 102.7
+    },
+    "0092": {
+      "cow_tag": "1128",
+      "temperature_f": null
+    },
+    "0093": {
+      "cow_tag": "1093",
+      "temperature_f": null
+    },
+    "0094": {
+      "cow_tag": "1126",
+      "temperature_f": null
+    },
+    "0095": {
+      "cow_tag": "1044",
+      "temperature_f": null
+    },
+    "0096": {
+      "cow_tag": "1041",
+      "temperature_f": null
+    },
+    "0097": {
+      "cow_tag": "1065",
+      "temperature_f": 103.5
+    }
+  }
+}

annotations/metadata.csv

@@ -0,0 +1,109 @@
+date,sequence_num,cow_tag,temperature_f,has_raw_thermal
+02_01,0001,1103,103.1,yes
+02_01,0002,1099,105.0,yes
+02_01,0006,1061,107.4,yes
+02_01,0007,1035,107.1,yes
+02_01,0008,1032,103.4,yes
+02_01,0009,1081,104.1,yes
+02_01,0010,1078,105.1,yes
+02_01,0011,1063,107.1,yes
+02_01,0012,1065,104.1,yes
+02_01,0013,1092,102.3,yes
+02_06,0001,1078,105.2,yes
+02_06,0002,1125,106.6,yes
+02_13,0001,1090,,yes
+02_13,0002,1115,103.9,yes
+02_13,0003,1119,,yes
+02_13,0004,1080,102.6,yes
+02_13,0005,1086,,yes
+02_13,0006,1070,,yes
+02_13,0007,1101,,yes
+02_13,0008,1053,,yes
+02_13,0009,1062,,yes
+02_13,0010,1056,,yes
+02_13,0011,1123,,yes
+02_13,0012,1055,,yes
+02_13,0013,1124,,yes
+02_13,0014,1095,103.0,yes
+02_13,0015,1106,,yes
+02_13,0016,1121,,yes
+02_13,0017,1063,,yes
+02_13,0018,1078,103.9,yes
+02_13,0019,1110,,yes
+02_13,0020,1108,,yes
+02_13,0021,1127,,yes
+02_13,0022,1096,,yes
+02_13,0023,1091,103.0,yes
+02_13,0024,1045,104.7,yes
+02_13,0025,1087,,yes
+02_13,0026,1113,,yes
+02_13,0027,1120,104.1,yes
+02_13,0028,1049,,yes
+02_13,0029,1117,,yes
+02_13,0030,1064,,yes
+02_13,0031,1043,,yes
+02_13,0032,1125,103.8,yes
+02_13,0033,1037,,yes
+02_13,0034,1048,,yes
+02_13,0035,1097,102.5,yes
+02_13,0036,1047,,yes
+02_13,0037,1082,102.4,no
+02_13,0038,1100,,no
+02_13,0039,1040,,no
+02_13,0040,1116,,no
+02_13,0041,1105,,no
+02_13,0042,1085,103.1,no
+02_13,0043,1084,,no
+02_13,0044,1112,,no
+02_13,0045,1058,,no
+02_13,0046,1031,,no
+02_13,0047,1083,,no
+02_13,0048,1089,104.2,no
+02_13,0049,1069,,no
+02_13,0050,1036,,no
+02_13,0051,1122,,no
+02_13,0052,1107,,no
+02_13,0053,1118,,no
+02_13,0054,1094,103.4,no
+02_13,0055,1034,,no
+02_13,0056,1077,,no
+02_13,0057,1092,,no
+02_13,0058,1074,,no
+02_13,0059,1054,,no
+02_13,0060,1072,,no
+02_13,0061,1111,,no
+02_13,0062,1109,,no
+02_13,0063,1042,,no
+02_13,0064,1059,,no
+02_13,0065,1067,,no
+02_13,0066,1088,,no
+02_13,0067,1066,,no
+02_13,0068,1032,,no
+02_13,0069,1098,,no
+02_13,0070,1035,,no
+02_13,0071,1046,,no
+02_13,0072,1081,,no
+02_13,0073,1039,,no
+02_13,0074,1114,,no
+02_13,0075,1102,,no
+02_13,0076,1075,,no
+02_13,0077,1038,,no
+02_13,0078,1052,,no
+02_13,0079,1060,,no
+02_13,0080,1099,,no
+02_13,0082,1061,,no
+02_13,0083,1050,,no
+02_13,0084,1079,,no
+02_13,0085,1103,,no
+02_13,0086,1104,103.7,no
+02_13,0087,1068,,no
+02_13,0088,1071,,no
+02_13,0089,1051,,no
+02_13,0090,1057,102.7,no
+02_13,0091,1076,102.7,no
+02_13,0092,1128,,no
+02_13,0093,1093,,no
+02_13,0094,1126,,no
+02_13,0095,1044,,no
+02_13,0096,1041,,no
+02_13,0097,1065,103.5,no