Model Card for rebotnix/rb_ship
🚢 Ship Detection in Maritime and Aerial Imagery – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.
This object detection model identifies ships and vessels in maritime and coastal imagery. It was trained on a custom-curated dataset with a broad range of ship types, sea conditions, backgrounds (negatives) and geographic environments. The model supports applications in coastal monitoring, maritime logistics, port authority surveillance, and marine environmental studies.
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 (Ship class)
- Trained on: REBOTNIX Aerial Ship 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 aerial imagery collected from:
- Open-source satellite archives
- Licensed drone surveys
- Custom annotated bounding boxes by REBOTNIX team
The model was trained to be robust across:
- Different vessel sizes (from small boats to cargo ships)
- Varied sea conditions (calm, stormy, cluttered)
- Partial occlusions
- Complex scenes (ports, coasts, open sea)
Intended Use
| ✅ Intended Use | ❌ Not Intended Use |
|---|---|
| Port traffic analysis | Naval combat systems |
| Maritime infrastructure monitoring | Submerged object detection |
| Shipping route analytics | Nighttime IR surveillance (unsupported) |
Limitations
- False positives may occur in heavy fog or extreme wave conditions
- May confuse small leisure boats with environmental debris
- Designed for daylight and optical imagery only
Usage Example
import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase
model_path= "./rb_ship.pth"
CLASS_NAMES = ["ship"]
model = RFDETRBase(pretrain_weights=model_path,num_classes=len(CLASS_NAMES))
image_path = "./example_ship1.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: [email protected]
🌐 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.