rb_graffiti / README.md

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license: cc-by-nc-sa-4.0
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tags:
  - objectdetection
  - graffiti
  - ai

Model Card for `rebotnix/rb_graffiti`

🖍️ Graffiti Detection on Urban Surfaces – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.

This object detection model identifies graffiti on urban surfaces such as walls, fences, and public structures. It has been trained on a curated dataset containing diverse graffiti styles, various urban environments, and lighting conditions. The model is designed to support research and automation use-cases in urban monitoring, smart city applications, and security.

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

🖍️ Example Predictions

Input Image	Detection Result


(More example visualizations coming soon)

Model Details

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

Chart

Dataset

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

Open-source cityscapes
Licensed urban surveillance footage
Custom annotated bounding boxes by REBOTNIX team

The model was trained to be robust across:

Different surfaces (walls, fences, public structures)
Various lighting conditions (daylight, night, low-light)
Different graffiti styles (tags, murals, street art)
Various urban backgrounds (residential, commercial, industrial)

Intended Use

✅ Intended Use	❌ Not Intended Use
Urban surveillance	Illegal activity detection
Graffiti removal planning	Non-urban object detection
Public infrastructure maintenance	Private property monitoring

Limitations

May miss graffiti on non-visible or heavily obscured surfaces
Less effective on small or intricate graffiti designs
Not optimized for non-urban environments

Usage Example

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

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

image_path = "./example_graffiti.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")

Contact

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

REBOTNIX
✉️ Email: communicate@rebotnix.com
🌐 Website: https://rebotnix.com

License

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

Model Card for rebotnix/rb_graffiti