---
license: cc-by-nc-sa-4.0
extra_gated_fields:
  full_name:
    type: text
    label: What is your full name?
    required: true
  email:
    type: text
    label: What is your email address?
    required: true
  company:
    type: text
    label: Which company or institution are you affiliated with?
    required: false
  intended_use:
    type: text
    label: Please describe your intended use of this model.
    required: true
  agreement:
    type: text
    label: >-
      Type "I agree" to confirm you have read and accept the license and usage
      conditions.
    required: true
tags:
- objectdetection
- coco
- ai
---

---

![Example input](./kineva_bg_coco1.jpg)


# Model Card for `rebotnix/rb_coco`

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

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`rb_coco` is a high-performance object detection 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

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## ✈️ Example Predictions

<!-- Placeholder for inference visualization images -->
| Input Image | Detection Result |
|-------------|------------------|
| <img src="./example_coco1.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_1.jpg" target="_blank"> <img src="./output_1.jpg" width="300"/></a> |
| <img src="./example_coco2.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_2.jpg" target="_blank"> <img src="./output_2.jpg" width="300"/></a> |
| <img src="./example_coco3.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_3.jpg" target="_blank"> <img src="./output_3.jpg" width="300"/></a> |
| <img src="./example_coco4.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_4.jpg" target="_blank"> <img src="./output_4.jpg" width="300"/></a> |
| <img src="./example_coco5.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_5.jpg" target="_blank"> <img src="./output_5.jpg" width="300"/></a> |
| <img src="./example_coco6.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_6.jpg" target="_blank"> <img src="./output_6.jpg" width="300"/></a> |
| <img src="./example_coco7.jpg" width="300"/> | <a href="./rb_coco/blob/main/output_7.jpg" target="_blank"> <img src="./output_7.jpg" width="300"/></a> |
_(More example visualizations coming soon)_

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## Model Details

- **Architecture**: KINEVA GOLD *(custom training head with optimized anchor boxes)*
- **Task**: Object Detection (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**: Gold Version 82.17M
- **Training Framework**: PyTorch + KINEVA + custom augmentation

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## Chart
![Example input](./chart.jpg)

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 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

More on COCO: [https://cocodataset.org](https://cocodataset.org)

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## Intended Use

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

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## ⚠️  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

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## Usage Example

```python

from kineva import KINEVA

#initialize model
model = KINEVA(model="models/kineva_gold_af_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")

```

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## Contact

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

**REBOTNIX**  
✉️ Email: [communicate@rebotnix.com](mailto:communicate@rebotnix.com)  
🌐 Website: [https://rebotnix.com](https://rebotnix.com)

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## 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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