Update README.md

README.md

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-# Street Sign Set 🚦
+<div align="center">
 
-This dataset was developed for the **Street Sign Sense** project, which achieved a final grade of **30/30** in the Machine Learning course. It is specifically designed and optimized for training state-of-the-art object detection models, such as **YOLOv12**, for the recognition of Italian and European road signs.
+# Street Sign Set
+[![License](https://img.shields.io/badge/License-CCBY4.0-darkgreen.svg?)]([LICENSE](https://creativecommons.org/licenses/by/4.0/))
+[![DOI](https://img.shields.io/badge/DOI-10.34740%2FKAGGLE%2FDS%2F8410752-blue)](https://doi.org/10.34740/KAGGLE/DS/8410752)
 
-## Dataset Description
+[![Kaggle](https://img.shields.io/badge/View_on-Kaggle-20BEFF?logo=kaggle)](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set)
+[![Hugging Face](https://img.shields.io/badge/View_on-Hugging_Face-FFD21E?logo=huggingface)](https://huggingface.co/datasets/AlessandroFerrante/StreetSignSet)
+[![Roboflow](https://img.shields.io/badge/View_on-Roboflow-purple?logo=Roboflow)](https://universe.roboflow.com/alessandros-workspace/street-sign-set-xzdde)
 
-The **Street Sign Set** is a comprehensive collection of traffic sign images, annotated in the **YOLO format**. It focuses on real-world driving conditions to ensure robustness in Advanced Driver Assistance Systems (ADAS).
+[![Ultralytics](https://img.shields.io/badge/Ultralytics-YOLO12-2596be?logo=ultralytics&logoColor=white)](https://github.com/ultralytics/ultralytics)
+[![Images](https://img.shields.io/badge/Images-%3E7.3k-success?style=flat&logo=image)](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set)
+[![Annotations](https://img.shields.io/badge/Annotations-%3E12k-success?style=flat&logo=image&logoColor=blue)](https://www.kaggle.com/datasets/ferrantealessandro/street-sign-set)
 
-**Street Sign Set** is a dataset with over **7300 images** designed for road sign detection in realistic contexts.
 
+[![Project-StreetSignSense](https://img.shields.io/badge/Project-StreetSignSense-007bff.svg)](https://github.com/AlessandroFerrante/StreetSignSense/)
+[![Badge Report PDF](https://img.shields.io/badge/📑-Technical_Report-white?logo=pdf&logoColor=white)](https://alessandroferrante.github.io/StreetSignSense/report/Report.pdf)
 
-#### 🏷️ Class Structure and Labels
+### High-Quality Traffic Sign Detection Dataset
 
-The dataset comprises **63 total classes**, organized into 5 macro-categories that define the label prefix: **prio** (priority), **forb** (prohibition), **info** (information), **warn** (warning), **mand** (mandatory).
+</div>
 
-Labeling examples: `prio_give_way`, `forb_speed_over_50`, `info_parking`, `warn_right_curve`, `mand_pass_left_right`.
+## 📂 Dataset Overview
 
-It includes 23 main classes identified as primary targets, including:
+**Street Sign Set** is a comprehensive dataset designed for road sign detection in realistic contexts. It serves as the foundation for the StreetSignSense project, enabling robust detection in diverse environmental conditions.
+
+The dataset is not perfectly balanced, reflecting the real-world frequency where some signs appear much more often than others.
+
+### 📊 Dataset Statistics
+
+* **Total Images:** **> 7,300** images.
+* **Classes:** **63** distinct classes.
+* **Macro-Categories:** 5 (Priority, Prohibition, Information, Warning, Mandatory).
+* **Format:** Standard YOLO annotations (`.txt`).
+
+## 🏷️ Class Structure and Labels
+
+The 63 classes are organized into **5 macro-categories** that define the label prefix:
+
+1. **prio** (Priority) - e.g., `prio_give_way`, `stop`
+2. **forb** (Prohibition) - e.g., `forb_speed_over_50`
+3. **info** (Information) - e.g., `info_parking`
+4. **warn** (Warning) - e.g., `warn_right_curve`
+5. **mand** (Mandatory) - e.g., `mand_pass_left_right`
+
+### Primary Targets (23 Main Classes)
+
+The dataset focuses on 23 main classes identified as primary targets, including:
 
 * **Speed limits:** 14 classes (e.g., 5–130 km/h).
-* **Prohibition signs:** 4 classes (e.g., no stopping and parking, no overtaking).
+* **Prohibition signs:** 4 classes (e.g., no stopping/parking, no overtaking).
 * **Priority signs:** 2 classes (e.g., give way, stop).
 * **Curves and crossings:** 3 classes (e.g., dangerous curves, pedestrian crossing).
 
-#### 🛠️ Hybrid Origin and Construction
+## 🛠️ Hybrid Origin and Construction
+
+This dataset is a result of a hybrid curation process:
 
-* **Base:** \~4000 images from a dataset available on Kaggle.
-* **Expansion:** \~3000 images manually integrated from external sources and street mapping services to cover underrepresented classes, subsequently manually labeled.
+* **Base:** ~4000 images sourced from existing Kaggle datasets.
+* **Expansion:** ~3000 images manually integrated from external sources and street mapping services to cover underrepresented classes. These were manually labeled to ensure quality.
 
-The dataset is not perfectly balanced due to the frequency of road signs in reality, so some signs appear more often than others.
+## ⚙️ Technical Specifications
 
-#### ⚙️ Technical Specifications
+* **Filename Scheme:** Rigorous logical scheme `class_name-n.jpg` (e.g., `prio_give_way-12.jpg`).
+* **Selective Data Augmentation:** Applied **only** to rare classes to mitigate class imbalance. Techniques include:
+  * Hue/Saturation/Brightness variations.
+  * Grayscale (23% probability).
+  * Blur and Noise simulation for adverse conditions.
 
-* **Format:** Standard **YOLO** annotations (.txt).
-* **Filename:** Rigorous logical scheme `class_name-n.jpg` (e.g., prio\_give\_way-12.jpg).
-* **Selective Data Augmentation:** Applied only to rare classes to mitigate imbalance. It includes variations in **Hue/Saturation/Brightness**, **Grayscale** (23%), **Blur**, and **Noise** to simulate adverse conditions.
+## 📥 Download & Access
 
+To keep the GitHub repository lightweight, the raw dataset is hosted on external platforms specialized for data versioning.
+
+
+
+## 🖊️ Citation
+
+If you use this dataset in your research, please cite it as follows:
+
+```
+@misc{alessandro_ferrante_2025,
+    title={Street Sign Set},
+    url={[https://www.kaggle.com/ds/8410752](https://www.kaggle.com/ds/8410752)},
+    DOI={10.34740/KAGGLE/DS/8410752},
+    publisher={Kaggle},
+    author={Alessandro Ferrante},
+    year={2025}
+}
+```
 
 ## Dataset Structure
 
@@ -70,4 +122,12 @@ The data is organized following the standard YOLO convention, making it ready fo
 │ ├── images/ # Test set for final evaluation
 │ └── labels/ # YOLO annotations
 ├── data.yaml # Dataset configuration file (classes names)
-└── dataset_analysis.csv # Detailed analysis of the dataset class distribution
+└── dataset_analysis.csv # Detailed analysis of the dataset class distribution
+```
+
+
+## 👨‍💻 Author
+
+[Alessandro Ferrante](https://alessandroferrante.net)
+
+Email: [streetsignsense@alessandroferrante.net](mailto:streetsignsense@alessandroferrante.net)