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Model Card for rebotnix/rb_trafficsign

🚀 Traffic Sign Detection – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.

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This object detection model identifies traffic signs in street imagery. It has been trained on a curated dataset containing a diverse set of traffic sign types, backgrounds (negatives), and lighting conditions. The model is designed to support research and automation use-cases in the fields of traffic monitoring, automotive in general and urban planning.

Developed and maintained by REBOTNIX, Germany, https://rebotnix.com

About KINEVA

KINEVA® is an automated training platform based on the MCP Agent system. It regularly delivers new visual computing models, all developed entirely from scratch. This approach enables the creation of customized models tailored to specific client requirements, which can be retrained and re-released as needed. The platform is particularly suited for applications that demand flexibility, adaptability, and technological precision—such as industrial image processing, smart city analytics, or automated object detection.

KINEVA is continuously evolving to meet the growing demands in the fields of artificial intelligence and machine vision. https://rebotnix.com/en/kineva

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🛑 Example Predictions

Input Image	Detection Result

(More example visualizations coming soon)

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Model Details

• Architecture: RF-DETR (custom training head with optimized anchor boxes)
• Task: Object Detection (Trafficsign class)
• Trained on: REBOTNIX Traffic Sign Dataset (proprietary)
• Format: PyTorch .pth + ONNX and trt export available on request
• Backbone: EfficientNet B3 (adapted)
• Training Framework: PyTorch + RF-DETR + custom augmentation

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Chart

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Dataset

The training dataset consists of high-resolution street imagery collected from:

• Open-source archives
• Custom annotated bounding boxes by REBOTNIX team

The model was trained to be robust across:

• Different backgrounds (urban, rural)
• Partial occlusions
• Different traffic sign types (danger signs, directional signs, regulatory signs)

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Intended Use

✅ Intended Use	❌ Not Intended Use
Autonomous vehicle training	Facial recognition
Driver assistance systems	Surveillance of individuals
Traffic infrastructure optimization	Weapon targeting
Road safety research	Non-traffic object classification

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Limitations

• False positives may occur in cluttered urban environments
• Not optimized for night-time or infrared imagery

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Usage Example

import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase

model_path= "./rb_trafficsign.pth"
CLASS_NAMES = ["trafficsign"]
model = RFDETRBase(pretrain_weights=model_path,num_classes=len(CLASS_NAMES))

image_path = "./example_trafficsign1.jpg"
image = Image.open(image_path)

detections = model.predict(image, threshold=0.35)

labels = [
    f"{CLASS_NAMES[class_id]} {confidence:.2f}"
    for class_id, confidence
    in zip(detections.class_id, detections.confidence)
]

print(labels)

annotated_image = image.copy()
annotated_image = sv.BoxAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

annotated_image.save("output_1.jpg")

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Contact

📫 For commercial use or re-training this model support, or dataset access, contact:

REBOTNIX
✉️ Email: [email protected]
🌐 Website: https://rebotnix.com

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License

This model is released under CC unless otherwise noted. For commercial licensing, please reach out to the contact email.

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