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Model Card for `rebotnix/rb_illegal_waste`

Illegal Waste Detection – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.

rb_illegal_waste is a specialized object detection model trained to detect illegal waste — improperly disposed garbage, dumped refuse, and unauthorized waste deposits found in public spaces, roadsides, forests, and urban areas. Designed for reliable detection across varying environments and lighting conditions, this model is well suited for environmental monitoring, municipal enforcement, and smart city waste management.

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

Model Details

Architecture: KINEVA SILVER (custom training head with optimized anchor boxes)
Task: Illegal Waste Detection (single class)
Trained on: Proprietary illegal waste dataset
Format: PyTorch .pth + ONNX and TRT export available on request
Training Framework: PyTorch + KINEVA + custom augmentation

We're happy to license or provide access to all intermediate weights for research or further development purposes. Please feel free to reach out.

Dataset

The model was trained on a proprietary dataset of illegal waste images, featuring:

Illegally dumped waste including household refuse, bulk items, and construction debris
Varied environments including roadsides, forests, urban areas, and industrial zones
Diverse lighting conditions including daylight, overcast, and shadows
Multiple waste types, sizes, and levels of occlusion

Intended Use

Intended Use	Not Intended Use
Illegal waste detection in images	Surveillance without human review
Environmental monitoring and compliance	Military / lethal applications
Municipal waste management and enforcement	Real-time safety-critical decisions without human oversight
Smart city cleanliness analytics

Limitations

May yield false positives in cluttered environments with legitimate waste containers
Not fine-tuned for thermal or night vision imagery
Occlusion and extreme viewing angles may reduce detection accuracy

Usage Example


from kineva import KINEVA

#initialize model
model = KINEVA(model="models/kineva_illegal_waste.pth")

#run inference on image
final_boxes, final_scores, final_labels = model.detect("example_illegal_waste_1.jpeg", threshold=0.35)

#draw detection
model.draw(final_boxes, final_scores, final_labels, output_path="./outputs/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.

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