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---
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language:
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- en
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license: cc-by-4.0
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library_name: ultralytics
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tags:
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- real-time
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- object-detection
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- yolo
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- yolov12
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- traffic-signs
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- autonomous-driving
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- adas
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datasets:
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- AlessandroFerrante/StreetSignSet
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metrics:
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- mAP
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- f1
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- precision
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- recall
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pipeline_tag: object-detection
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---
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<div align="center">
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# StreetSignSenseYOLO12m
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[](https://github.com/ultralytics/ultralytics)
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[](https://github.com/ultralytics/ultralytics)
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[](https://github.com/sunsmarterjie/yolov12)
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[](https://www.python.org/)
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[](https://pytorch.org/)
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[](LICENSE)
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[](LICENSE)
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[](https://github.com/AlessandroFerrante/StreetSignSense/)
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[](https://alessandroferrante.github.io/StreetSignSense/report/Report.pdf)
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[](https://github.com/AlessandroFerrante/StreetSignSense/releases)
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[](https://github.com/AlessandroFerrante/StreetSignSense/releases)
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[](https://github.com/AlessandroFerrante/StreetSignSense/releases)
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[](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12n/)
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[](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12s/)
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[](https://www.kaggle.com/models/ferrantealessandro/streetsignsensey12m/)
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[](https://huggingface.co/AlessandroFerrante/StreetSignSenseY12n)
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[](https://HuggingFace.co/AlessandroFerrante/StreetSignSenseY12s)
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[](https://huggingface.co/AlessandroFerrante/StreetSignSenseY12m)
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</div>
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---
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# Model Summary
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**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.
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## Usage
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### Live Demo
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You can test this model instantly in your browser without any setup: 👉 **[Interactive Web Demo](http://alessandroferrante.github.io/StreetSignSense)**
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#### Python
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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.
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### Code Snippet (Python)
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```python
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from ultralytics import YOLO
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# Load the model
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model = YOLO('path/to/streetsignsense-yolo12m.pt') # Replace with the downloaded model path
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# Run inference on an image
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results = model.predict(source='path/to/image.jpg')
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# Show results
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results[0].show()
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```
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**Inputs:** Images (RGB) of various resolutions (model trained at standard YOLO resolutions, e.g., 640x640).
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**Outputs:** List of `Results` objects containing bounding boxes (`xyxy`), class IDs, and confidence scores.
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``` text
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/StreetSignSenseY12m
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├── .gitattributes
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├── README.md
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├── streetsignsense-yolo12m.pt
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└── metrics/ # metrics image folder
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```
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## System
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**Standalone Model:** Yes, this is a standalone object detection model.
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**Input Requirements:** Standard RGB images. No specific metadata required.
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**Downstream Dependencies:** The output (detected classes and locations) is intended to be used by decision-making logic in ADAS simulations or autonomous driving pipelines.
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## Implementation requirements
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**Hardware:** Training was performed on Kaggle Notebooks using NVIDIA GPUs (e.g., Tesla P100 or T4).
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**Software:** PyTorch, Ultralytics YOLO framework.
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**Compute:**
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* **Training Time:** 13h 23m 53s · GPU T4 x (depending on epochs).
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* **Inference:** Capable of real-time performance (>30 FPS) on modern GPUs.
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# Model Characteristics
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## Model initialization
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**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.
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## Model stats
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**Architecture:** YOLO12m (Medium).
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**Characteristics:** Utilizes attention-centric mechanisms to improve feature extraction compared to previous YOLO versions.
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**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.
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## Other details
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**Precision:** Trained using Mixed Precision (AMP).
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**Pruning/Quantization:** The uploaded weights are standard FP32/FP16. No post-training quantization has been applied yet.
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# Data Overview
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## Training data
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The model was trained on the **Street Sign Set** (available on Kaggle).
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* **Source:** A combination of public datasets and manually collected/annotated images.
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* **Size:** Contains thousands of images with bounding box annotations.
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* **Classes:** 63 specific traffic sign classes (speed limits, warnings, prohibitions, etc.).
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* **Preprocessing:** Images were resized, and data augmentation (Mosaic, scaling, color adjustments) was applied during training to improve robustness.
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## Demographic groups
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**N/A:** The dataset consists of street signs and environmental imagery. No human demographic data is involved or analyzed.
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## Evaluation data
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The dataset was split into:
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* **Train:** 70-80%
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* **Validation:** 10-20%
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* **Test:** 10%
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**Differences:** The test set contains unseen images from different environmental conditions to test generalization.
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# Evaluation Results
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* **Overview Risultati:**
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* **Confusion Matrix:**
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#### Detailed Curves (Small)
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| Precision-Recall | F1 Score |
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| :----------------------------------------: | :----------------------------------------: |
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| **Precision** | **Recall** |
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## Summary
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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.
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* **Detailed Metrics:** Please refer to the training graphs (F1-score, Precision-Recall curve) included in the attached notebooks.
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## Subgroup evaluation results
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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.
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## Fairness
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**Definition:** Fairness in this context is defined as the model's ability to detect signs regardless of background clutter or slight occlusions.
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**Results:** The model shows robust performance in standard driving scenarios.
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## Usage limitations
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* **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.
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* **Occlusion:** Signs that are more than 50% occluded may not be detected reliably.
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* **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.
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## Ethics
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**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.
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**Privacy:** The dataset focuses on public street signs. Any incidental faces or license plates in the background are not the target of this model.
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## 👨💻 Author
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[Alessandro Ferrante](https://alessandroferrante.net)
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Email: [streetsignsense@alessandroferrante.net](mailto:streetsignsense@alessandroferrante.net)
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