Update README.md

README.md

@@ -10,7 +10,64 @@ tags:
 - adas
 - computer-vision
 - yolo
+- yolov12
+pretty_name: Street Sign Set
 size_categories:
 - 1K<n<10K
-pretty_name: Street Sign Sense Dataset
----
+---
+
+# Street Sign Set 🚦
+
+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.
+
+## Dataset Description
+
+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).
+
+**Street Sign Set** is a dataset with over **7300 images** designed for road sign detection in realistic contexts.
+
+
+#### 🏷️ Class Structure and Labels
+
+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).
+
+Labeling examples: `prio_give_way`, `forb_speed_over_50`, `info_parking`, `warn_right_curve`, `mand_pass_left_right`.
+
+It includes 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).
+* **Priority signs:** 2 classes (e.g., give way, stop).
+* **Curves and crossings:** 3 classes (e.g., dangerous curves, pedestrian crossing).
+
+#### 🛠️ Hybrid Origin and Construction
+
+* **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.
+
+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
+
+* **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.
+
+
+## Dataset Structure
+
+The data is organized following the standard YOLO convention, making it ready for immediate training:
+
+```text
+.
+├── train/
+│ ├── images/ # Training set
+│ └── labels/ # YOLO annotations
+├── val/
+│ ├── images/ # Validation set
+│ └── labels/ # YOLO annotations
+├── test/
+│ ├── 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