Update README.md

README.md

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----
-license: cc-by-nc-4.0
-language:
-- en
-pipeline_tag: object-detection
-library_name: mmdetection
----
-## Introduction
-We introduce a real-world aerial view dataset, UGRC, captured in Utah (USA). The dataset has ground sampling distance (GSD) of 12.5 cm per px and have been sampled to 112 px × 112 px image size. For data annotation, we label only the small vehicle centers. To leverage the abundance of bounding box-based open-source object detection frameworks, we define a fixed-size ground truth bounding box of 42.36 px × 42.36 px center at each vehicle. Annotations are provided in COCO format [x, y, w, h], where "small" in the annotation json files denotes the small vehicle class and (x, y) denotes the top-left corner of the bounding box. We use AP50 as evaluation metrics.
-
-## Model Usage
-This folder contains four detectors trained on Real UGRC data and tested on Real UGRC data, along with configuration files we use for training and testing.
-
-## References
-
-➡️ **Paper:** [Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision](https://arxiv.org/abs/2507.20976)  
-➡️ **Project Page:** [Webpage](https://humansensinglab.github.io/AGenDA/)  
-➡️ **Data:** [AGenDA](https://github.com/humansensinglab/AGenDA/tree/main/Data)  
+---
+license: cc-by-nc-4.0
+language:
+- en
+pipeline_tag: object-detection
+library_name: mmdetection
+---
+## Introduction
+We introduce a real-world aerial view dataset, UGRC, captured in Utah (USA). The dataset has ground sampling distance (GSD) of 12.5 cm per px and has been sampled to 112 px × 112 px image size. For data annotation, we label only the small vehicle centers. To leverage the abundance of bounding box-based open-source object detection frameworks, we define a fixed-size ground truth bounding box of 42.36 px × 42.36 px centered at each vehicle. Annotations are provided in COCO format [x, y, w, h], where "small" in the annotation json files denotes the small vehicle class and (x, y) denotes the top-left corner of the bounding box. We use AP50 as the evaluation metric.
+
+## Model Usage
+This folder contains four detectors trained on Real UGRC data and tested on Real UGRC data, along with configuration files we use for training and testing.
+
+## References
+
+➡️ **Paper:** [Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision](https://arxiv.org/abs/2507.20976)  
+➡️ **Project Page:** [Webpage](https://humansensinglab.github.io/AGenDA/)  
+➡️ **Data:** [AGenDA](https://github.com/humansensinglab/AGenDA/tree/main/Data)