Upload StreetSignSense YOLO12m model and metrics

.gitattributes

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README.md

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 ---
+language:
+- en
 license: cc-by-4.0
+library_name: ultralytics
+tags:
+- real-time
+- object-detection
+- yolo
+- yolov12
+- traffic-signs
+- autonomous-driving
+- adas
+datasets:
+- AlessandroFerrante/StreetSignSet
+metrics:
+- mAP
+- f1
+- precision
+- recall
+pipeline_tag: object-detection
 ---
+
+<div  align="center">
+
+# StreetSignSenseYOLO12m
+
+[![Ultralytics  8.3.229 ](https://img.shields.io/badge/Ultralytics-8.3.229-lightblue?logo=ultralytics&logoColor=white)](https://github.com/ultralytics/ultralytics)
+[![Ultralytics Github](https://img.shields.io/badge/Ultralytics-Github-darkgreen?logo=ultralytics&logoColor=white)](https://github.com/ultralytics/ultralytics)
+[![Ultralytics YOLO12](https://img.shields.io/badge/Ultralytics-YOLO12-8A2BE2?logo=ultralytics&logoColor=white)](https://github.com/sunsmarterjie/yolov12)
+
+[![Python 3.11.13 ](https://img.shields.io/badge/Python-3.11.13-blue?logo=python&logoColor=white)](https://www.python.org/)
+[![PyTorch 2.6.0](https://img.shields.io/badge/PyTorch-2.6.0-EE4C2C?logo=pytorch&logoColor=white)](https://pytorch.org/)
+[![License](https://img.shields.io/badge/License-MIT-green.svg?)](LICENSE)
+[![License](https://img.shields.io/badge/License-CC_BY_4.0-orange.svg?)](LICENSE)
+
+[![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)
+
+[![GitHub Release](https://img.shields.io/badge/GitHub-StreetSignSenseY12n-181717?logo=github)](https://github.com/AlessandroFerrante/StreetSignSense/releases)
+[![GitHub Release](https://img.shields.io/badge/GitHub-StreetSignSenseY12s-181717?logo=github)](https://github.com/AlessandroFerrante/StreetSignSense/releases)
+[![GitHub Release](https://img.shields.io/badge/GitHub-StreetSignSenseY12m-181717?logo=github)](https://github.com/AlessandroFerrante/StreetSignSense/releases)
+
+[![Model-StreetSignSense](https://img.shields.io/badge/KaggleModel-StreetSignSenseY12n-20BEFF.svg?logo=kaggle&logoColor=white)](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12n/)
+[![Model-StreetSignSense](https://img.shields.io/badge/KaggleModel-StreetSignSenseY12s-20BEFF.svg?logo=kaggle&logoColor=white)](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12s/)
+[![Model-StreetSignSense](https://img.shields.io/badge/KaggleModel-StreetSignSenseY12m-20BEFF.svg?logo=kaggle&logoColor=white)](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12m/)
+
+[![Model-StreetSignSense](https://img.shields.io/badge/HuggingFace-StreetSignSenseY12n-FFD21E.svg?logo=huggingface)](https://huggingface.co/AlessandroFerrante/StreetSignSenseY12n)
+[![Model-StreetSignSense](https://img.shields.io/badge/KaggleModel-StreetSignSenseY12s-FFD21E.svg?logo=huggingface)](https://HuggingFace.co/AlessandroFerrante/StreetSignSenseY12s)
+[![Model-StreetSignSense](https://img.shields.io/badge/HuggingFace-StreetSignSenseY12m-FFD21E.svg?logo=huggingface)](https://huggingface.co/AlessandroFerrante/StreetSignSenseY12m)
+
+</div>
+
+---
+
+# Model Summary
+
+**Street Sign Sense (YOLO12m)** is an object detection model designed to identify and classify traffic signs in real-time. Based on the advanced **YOLO12 Medium** architecture, this model balances high accuracy with computational efficiency, making it suitable for Advanced Driver Assistance Systems (ADAS) research. It has been trained on the custom **Street Sign Set**, covering **63 distinct classes** of traffic signs.
+
+## Usage
+
+### Live Demo
+
+You can test this model instantly in your browser without any setup: 👉 **[Interactive Web Demo](http://alessandroferrante.github.io/StreetSignSense)**
+
+#### Python
+
+This model can be used with the Ultralytics framework or the official YOLO12 repository. It takes an image as input and outputs bounding boxes with class labels and confidence scores.
+
+### Code Snippet (Python)
+
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO('path/to/streetsignsense-yolo12m.pt')  # Replace with the downloaded model path
+
+# Run inference on an image
+results = model.predict(source='path/to/image.jpg')
+
+# Show results
+results[0].show()
+
+```
+
+**Inputs:** Images (RGB) of various resolutions (model trained at standard YOLO resolutions, e.g., 640x640).
+**Outputs:** List of `Results` objects containing bounding boxes (`xyxy`), class IDs, and confidence scores.
+``` text
+/StreetSignSenseY12m
+├── .gitattributes   
+├── README.md  
+├── streetsignsense-yolo12m.pt   
+└── metrics/ # metrics image folder
+```
+
+## System
+
+**Standalone Model:** Yes, this is a standalone object detection model.
+**Input Requirements:** Standard RGB images. No specific metadata required.
+**Downstream Dependencies:** The output (detected classes and locations) is intended to be used by decision-making logic in ADAS simulations or autonomous driving pipelines.
+
+## Implementation requirements
+
+**Hardware:** Training was performed on Kaggle Notebooks using NVIDIA GPUs (e.g., Tesla P100 or T4).
+**Software:** PyTorch, Ultralytics YOLO framework.
+**Compute:**
+
+* **Training Time:** 13h 23m 53s · GPU T4 x (depending on epochs).
+* **Inference:** Capable of real-time performance (&gt;30 FPS) on modern GPUs.
+
+# Model Characteristics
+
+## Model initialization
+
+**Fine-tuned:** The model was initialized with pre-trained COCO weights (Transfer Learning) and then fine-tuned on the "Street Sign Sense" dataset to specialize in traffic sign detection.
+
+## Model stats
+
+**Architecture:** YOLO12m (Medium).
+**Characteristics:** Utilizes attention-centric mechanisms to improve feature extraction compared to previous YOLO versions.
+**Size:** Medium-sized model, offering a trade-off between the speed of the 'Nano/Small' versions and the raw accuracy of the 'Large/X-Large' versions.
+
+## Other details
+
+**Precision:** Trained using Mixed Precision (AMP).
+**Pruning/Quantization:** The uploaded weights are standard FP32/FP16. No post-training quantization has been applied yet.
+
+# Data Overview
+
+## Training data
+
+The model was trained on the **Street Sign Set** (available on Kaggle).
+
+* **Source:** A combination of public datasets and manually collected/annotated images.
+* **Size:** Contains thousands of images with bounding box annotations.
+* **Classes:** 63 specific traffic sign classes (speed limits, warnings, prohibitions, etc.).
+* **Preprocessing:** Images were resized, and data augmentation (Mosaic, scaling, color adjustments) was applied during training to improve robustness.
+
+## Demographic groups
+
+**N/A:** The dataset consists of street signs and environmental imagery. No human demographic data is involved or analyzed.
+
+## Evaluation data
+
+The dataset was split into:
+
+* **Train:** 70-80%
+* **Validation:** 10-20%
+* **Test:** 10%
+  **Differences:** The test set contains unseen images from different environmental conditions to test generalization.
+
+# Evaluation Results
+* **Overview Risultati:**
+  ![Results Small](metrics/results.png)
+* **Confusion Matrix:**
+  ![Confusion Matrix Small](metrics/confusion_matrix_normalized.png)
+
+#### Detailed Curves (Small)
+
+|              Precision-Recall              |                  F1 Score                  |
+| :----------------------------------------: | :----------------------------------------: |
+| ![PR Curve Small](metrics/BoxPR_curve.png) | ![F1 Curve Small](metrics/BoxF1_curve.png) |
+|            **Precision**            |              **Recall**              |
+|  ![P Curve Small](metrics/BoxP_curve.png)  |  ![R Curve Small](metrics/BoxR_curve.png)  |
+
+## Summary
+
+The model achieves high Mean Average Precision (mAP) on the test set, demonstrating strong capabilities in detecting small objects (traffic signs at a distance) and operating in varied lighting conditions.
+
+* **Detailed Metrics:** Please refer to the training graphs (F1-score, Precision-Recall curve) included in the attached notebooks.
+
+## Subgroup evaluation results
+
+Performance is generally consistent across major classes (e.g., Speed Limits, Stop signs). However, classes with significantly fewer samples in the dataset may show slightly lower recall.
+
+## Fairness
+
+**Definition:** Fairness in this context is defined as the model's ability to detect signs regardless of background clutter or slight occlusions.
+**Results:** The model shows robust performance in standard driving scenarios.
+
+## Usage limitations
+
+* **Lighting:** Performance may degrade in extreme low-light conditions (night without streetlights) or heavy weather (dense fog/heavy rain) if not sufficiently represented in the training data.
+* **Occlusion:** Signs that are more than 50% occluded may not be detected reliably.
+* **Geography:** The model is trained primarily on European/International standard signs; it may not recognize signs specific to other regions that differ significantly in shape or color.
+
+## Ethics 
+
+**Safety:** This model is for research and educational purposes (ADAS development). It should **not** be used as the sole system for controlling a real vehicle on public roads without extensive safety validation and redundancy.
+**Privacy:** The dataset focuses on public street signs. Any incidental faces or license plates in the background are not the target of this model.
+
+## 👨‍💻 Author
+
+[Alessandro Ferrante](https://alessandroferrante.net)
+
+Email: [streetsignsense@alessandroferrante.net](mailto:streetsignsense@alessandroferrante.net)

metrics/results.csv

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streetsignsense-yolo12m.pt

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