---
language: en
license: agpl-3.0
tags:
- yolov11
- object-detection
- computer-vision
- yolo
- vessel
- boat
- ship
- satellite
- remote-sensing
- google-earth
task: object-detection
library: ultralytics
base_model:
- Ultralytics/YOLO11
---

# Vessel Detection Model

This model performs vessel detection using YOLOv11 architecture. Two versions are available:
- YOLOv11-nano (public)
- YOLOv11-xlarge (private, enterprise)

## Model Description

The model is trained to detect vessels in maritime imagery. It uses the YOLOv11 architecture with improvements in detection accuracy and speed.

## Performance

**YOLOv11-nano (Public Version)**
- mAP50: 0.474
- mAP50-95: 0.192
- Precision: 0.587
- Recall: 0.476

**YOLOv11-xlarge (Enterprise Version)**
- mAP50: 0.579
- mAP50-95: 0.318
- Precision: 0.612
- Recall: 0.571

For access to the enterprise version, please contact admin@truth.phd

## Usage
```bash
pip install huggingface_hub ultralytics matplotlib
```
```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
import matplotlib.pyplot as plt

# Download the model file and a sample image from Hugging Face
repo_id = "truthdotphd/vessel-detection"
model_path = hf_hub_download(repo_id=repo_id, filename="model.pt")
image_path = hf_hub_download(repo_id=repo_id, filename="vessels.jpg")

# Load the model
model = YOLO(model_path)

# Use the model for inference
results = model(image_path)
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))  # Convert BGR to RGB for matplotlib
plt.axis('off')
plt.show()
```
![Vessel Detection Predictions](vessels-preds.jpg)
## Limitations

- Performance may vary depending on image quality and lighting conditions
- Optimized for daytime maritime imagery
- Detection accuracy may decrease in crowded scenes

## Training Data

The model was trained on a proprietary dataset of maritime vessel images. The dataset includes various vessel types under different weather and lighting conditions.

## Training Procedure

- Architecture: YOLOv11
- Training Framework: Ultralytics
- Hardware: NVIDIA GPUs
- Optimization: AdamW optimizer

For enterprise solutions and access to YOLOv11-xlarge, please contact admin@truth.phd