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README.md

@@ -1,45 +1,53 @@
-# OpenThermalPose and OpenThermalPose2 Datasets Card
+# OpenThermalPose and OpenThermalPose2: Open-Source Annotated Thermal Human Pose Datasets
 
-## Dataset Summary
-
-This dataset card describes OpenThermalPose and its extension, OpenThermalPose2, two open-source datasets for thermal human pose estimation.  These datasets contain annotated thermal images of humans performing various activities, including fitness exercises, multiple-person interactions, and walking in diverse outdoor and indoor environments.  Annotations include bounding boxes and 17 keypoints per human instance, consistent with the MS COCO Keypoint dataset.  Pre-trained YOLOv8-pose and YOLO11-pose models are also provided as baselines.
+This Hugging Face dataset card describes the OpenThermalPose and OpenThermalPose2 datasets, two open-source annotated thermal human pose datasets created by Kuzdeuov et al.  These datasets are useful for research in human pose estimation using thermal imagery.
 
 ## OpenThermalPose
 
-* **Images:** 6,090
-* **Subjects:** 31
-* **Annotated Instances:** 14,315
-* **Annotations:** Bounding boxes, 17 keypoints
-* **Activities:** Fitness exercises, multiple-person activities, outdoor walking
-* **Conditions:** Varied weather conditions and locations
-* **Pre-trained Models:** [Link to Pre-trained Models](https://drive.google.com/file/d/1BS2AB6wGRjZ8Tvz44_jkURR6moRpVS1j/view?usp=sharing)
+**Summary:** OpenThermalPose provides 6,090 images of 31 subjects, totaling 14,315 annotated human instances. Each instance includes bounding boxes and 17 anatomical keypoints, consistent with the MS COCO Keypoint dataset.  The dataset features diverse scenarios including fitness exercises, multiple-person activities, and outdoor walking under varying weather conditions.  Baseline results using YOLOv8-pose models (nano, small, medium, large, and x-large) are also provided.
 
 
 ## OpenThermalPose2
 
-* **Images:** 11,391
-* **Subjects:** 170
-* **Annotated Instances:** 21,125
-* **Annotations:** Bounding boxes, 17 keypoints
-* **Activities:** Fitness exercises, multiple-person activities, indoor sitting, outdoor walking
-* **Conditions:** Varied weather conditions and locations, indoor and outdoor settings
-* **Pre-trained Models:** [Link to Pre-trained Models](https://drive.google.com/file/d/19bvKSNKs3Z-8EFSJcTMaI-MdkNwVKh1f/view?usp=sharing)
+**Summary:** OpenThermalPose2 extends the original dataset with significantly more data, subjects, and poses.  It contains 11,391 images of 170 subjects and 21,125 annotated human instances.  The expanded dataset includes additional scenarios like persons sitting indoors, maintaining the diversity of fitness exercises, multiple-person activities, and outdoor walking under various weather conditions.  YOLOv8-pose and YOLO11-pose models (nano, small, medium, large, and x-large) were trained and evaluated on this expanded dataset.
+
+
+## Dataset Statistics
+
+| Dataset       | # Images | # Subjects | # Instances | Keypoints | Scenarios                               |
+|---------------|----------|------------|-------------|------------|-------------------------------------------|
+| OpenThermalPose | 6,090     | 31         | 14,315       | 17         | Fitness exercises, multiple-person activities, outdoor walking |
+| OpenThermalPose2| 11,391    | 170        | 21,125      | 17         | Fitness exercises, multiple-person activities, indoor sitting, outdoor walking |
 
 
-## Dataset Examples
+## Citations
 
-**(Images showcasing various activities and settings are included here.  Refer to the original text for image links.)**
+**OpenThermalPose:**
 
+```bibtex
+@INPROCEEDINGS{10581992,
+  author={Kuzdeuov, Askat and Taratynova, Darya and Tleuliyev, Alim and Varol, Huseyin Atakan},
+  booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)}, 
+  title={OpenThermalPose: An Open-Source Annotated Thermal Human Pose Dataset and Initial YOLOv8-Pose Baselines}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1-8},
+  keywords={Privacy;Annotations;Source coding;Pose estimation;Lighting;Medical services;Motion capture},
+  doi={10.1109/FG59268.2024.10581992}}
+```
 
-##  Model Baselines
+**OpenThermalPose2:**
 
-YOLOv8-pose and YOLO11-pose (nano, small, medium, large, and x-large) models were trained and evaluated on both datasets.  Further details on training and evaluation can be found in the provided preprints.  The models utilize the Ultralytics library.
+```bibtex
+Askat Kuzdeuov, Miras Zakaryanov, Alim Tleuliyev, and Huseyin Atakan Varol. OpenThermalPose2: Extending the Open-Source Annotated Thermal Human Pose Dataset With More Data, Subjects, and Poses. TechRxiv. October 18, 2024, DOI: 10.36227/techrxiv.172926774.47783447/v1.
+```
 
+## Repository
 
-## Additional Information
+[https://github.com/IS2AI/OpenThermalPose](https://github.com/IS2AI/OpenThermalPose)
 
-* **Preprint (OpenThermalPose):** [TechRxiv Link](https://www.techrxiv.org/users/682600/articles/741508-openthermalpose-an-open-source-annotated-thermal-human-pose-dataset-and-initial-yolov8-pose-baselines)
-* **Preprint (OpenThermalPose2):** [TechRxiv Link](https://www.techrxiv.org/users/682600/articles/1231799-openthermalpose2-extending-the-open-source-annotated-thermal-human-pose-dataset-with-more-data-subjects-and-poses)
-* **Ultralytics Documentation:** [Link to Ultralytics Docs](https://docs.ultralytics.com/tasks/pose/)
 
+##  Images
 
+**(Images from the GitHub repository showing example scenarios are included in the original content, but are not reproduced here due to the limitations of this response format.)**