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README.md

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+---
+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.