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license: cc-by-nc-sa-4.0
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tags:
  - object segmentation
  - coco
  - ai

Model Card for `rebotnix/kineva_silver_segmentation_coco`

🎯 General Object Segmentation on COCO Dataset – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.

rb_coco is a high-performance object segmentation model trained on the COCO dataset, supporting detection across a wide range of object categories (e.g., people, vehicles, animals, furniture, etc.). Designed for robust performance in varied lighting, scale, and background conditions, this model suits research, prototyping, and applied AI in urban monitoring, automation, and more.

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: KINEVA SILVER SEGMENTATION*(custom training head with optimized anchor boxes)*
Task: Object Segmentation (80 COCO categories, e.g. person, car, dog, bicycle)
Trained on: COCO (Common Objects in Context) dataset
Format: PyTorch .pth + ONNX and trt export available on request
Parameters: Silber Version
Training Framework: PyTorch + KINEVA + custom augmentation

📦 Dataset

The model was trained exclusively on the COCO dataset, which includes:

80 object categories
Over 330,000 images
Diverse backgrounds and lighting conditions
Complex scenes with multiple overlapping objects

Intended Use

✅ Intended Use	❌ Not Intended Use
General object segmentation on images	Surveillance without human review
Academic research & prototyping	Military / lethal applications
Smart city & automation projects	Real-time tracking of people in critical situations

⚠️ Limitations

May yield false positives in highly cluttered environments
Not fine-tuned for thermal or night vision
Object occlusion and scale variance may reduce detection accuracy

Usage Example


from kineva import KINEVA_SEG

#initialize model
model = KINEVA_SEG(model="models/kineva_silver_segmentation_coco.pth")

#run inference on image
final_boxes, final_scores, final_labels = model.detect("example_coco1.jpg", 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.

Model Card for rebotnix/kineva_silver_segmentation_coco