---
library_name: tensorflow
license: mit
tags:
- oil-spill-detection
- semantic-segmentation
- satellite-imagery
- tensorflow
- keras
- computer-vision
- environmental-monitoring
metrics:
- f1-score
model-index:
- name: DeepLabV3+ Oil Spill Detection
  results:
  - task:
      type: semantic-segmentation
      name: Oil Spill Detection
    dataset:
      name: Oil Spill Satellite Images
      type: satellite-imagery
    metrics:
    - type: f1-score
      value: 0.9668
      name: F1 Score
---

# DeepLabV3+ Oil Spill Detection - Oil Spill Detection

## Model Description

High-accuracy DeepLabV3+ model for oil spill detection from satellite images. Best performance for critical applications.

## Model Details

- **Model Size**: 204.56 MB
- **F1 Score**: 0.9668
- **Input Shape**: (256, 256, 3) - RGB satellite images
- **Output Shape**: (256, 256, 5) - 5-class semantic segmentation
- **Classes**: Background, Oil Spill, Ships, Look-alike, Wakes
- **Format**: TensorFlow Keras (.keras)

## Usage

```python
import tensorflow as tf
import numpy as np
from PIL import Image

# Load the model
model = tf.keras.models.load_model('model.keras', compile=False)

# Preprocess image
def preprocess_image(image_path):
    image = Image.open(image_path).convert('RGB')
    image = image.resize((256, 256))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

# Make prediction
image_array = preprocess_image('your_image.jpg')
prediction = model.predict(image_array)
```

## Model Performance

This model achieves an F1 score of 0.9668 on the oil spill detection task, making it suitable for production deployment in environmental monitoring systems.

## Deployment

This model is optimized for production deployment with:
- Fast inference times
- Memory-efficient architecture
- Support for batch processing
- Compatible with TensorFlow Serving

## License

MIT License - Free for academic and commercial use.