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--- |
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license: cc-by-nc-sa-4.0 |
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extra_gated_fields: |
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full_name: |
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type: text |
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label: What is your full name? |
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required: true |
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email: |
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type: text |
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label: What is your email address? |
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required: true |
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company: |
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type: text |
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label: Which company or institution are you affiliated with? |
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required: false |
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intended_use: |
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type: text |
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label: Please describe your intended use of this model. |
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required: true |
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agreement: |
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type: text |
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label: >- |
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Type "I agree" to confirm you have read and accept the license and usage |
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conditions. |
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required: true |
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tags: |
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- objectdetection |
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- graffiti |
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- ai |
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--- |
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--- |
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# Model Card for `rebotnix/rb_graffiti` |
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> 🖍️ **Graffiti Detection on Urban Surfaces** – Trained by KINEVA, Built by REBOTNIX, Germany |
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Current State: in production and re-training. |
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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**. |
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Developed and maintained by **REBOTNIX**, Germany, https://rebotnix.com |
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# About KINEVA |
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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. |
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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 |
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<!-- Placeholder for inference visualization images --> |
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| Input Image | Detection Result | |
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|-------------|------------------| |
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| <img src="./example_graffiti1.jpg" width="300"/> | <img src="./output_1.jpg" width="300"/> | |
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| <img src="./example_graffiti2.jpg" width="300"/> | <img src="./output_2.jpg" width="300"/> | |
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_(More example visualizations coming soon)_ |
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--- |
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## Model Details |
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- **Architecture**: RF-DETR *(custom training head with optimized anchor boxes)* |
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- **Task**: Object Detection (Graffiti class) |
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- **Trained on**: REBOTNIX Graffiti Dataset (proprietary) |
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- **Format**: PyTorch `.pth` + ONNX and trt export available on request |
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- **Backbone**: EfficientNet B3 (adapted) |
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- **Training Framework**: PyTorch + RF-DETR + custom augmentation |
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--- |
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## Chart |
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## Dataset |
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The training dataset consists of **high-resolution urban imagery** collected from: |
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- Open-source cityscapes |
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- Licensed urban surveillance footage |
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- Custom annotated bounding boxes by REBOTNIX team |
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The model was trained to be robust across: |
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- Different surfaces (walls, fences, public structures) |
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- Various lighting conditions (daylight, night, low-light) |
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- Different graffiti styles (tags, murals, street art) |
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- Various urban backgrounds (residential, commercial, industrial) |
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--- |
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## Intended Use |
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| ✅ Intended Use | ❌ Not Intended Use | |
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|----------------|---------------------| |
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| Urban surveillance | Illegal activity detection | |
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| Graffiti removal planning | Non-urban object detection | |
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| Public infrastructure maintenance | Private property monitoring | |
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--- |
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## Limitations |
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- May miss graffiti on **non-visible or heavily obscured surfaces** |
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- Less effective on **small or intricate graffiti designs** |
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- Not optimized for non-urban environments |
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--- |
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## Usage Example |
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```python |
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import supervision as sv |
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from PIL import Image |
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from rfdetr import RFDETRBase |
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model_path= "./rb_graffiti.pth" |
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CLASS_NAMES = ["graffiti"] |
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model = RFDETRBase(pretrain_weights=model_path,num_classes=len(CLASS_NAMES)) |
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image_path = "./example_graffiti.jpg" |
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image = Image.open(image_path) |
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detections = model.predict(image, threshold=0.35) |
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labels = [ |
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f"{CLASS_NAMES[class_id]} {confidence:.2f}" |
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for class_id, confidence |
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in zip(detections.class_id, detections.confidence) |
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] |
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print(labels) |
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annotated_image = image.copy() |
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annotated_image = sv.BoxAnnotator().annotate(annotated_image, detections) |
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annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels) |
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annotated_image.save("./output_1.jpg") |
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``` |
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--- |
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## Contact |
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📫 For commercial use or re-training this model support, or dataset access, contact: |
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**REBOTNIX** |
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✉️ Email: [communicate@rebotnix.com](mailto:communicate@rebotnix.com) |
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🌐 Website: [https://rebotnix.com](https://rebotnix.com) |
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--- |
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## License |
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This model is released under **CC-BY-NC-SA** unless otherwise noted. For commercial licensing, please reach out to the contact email. |
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