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.

deytiyas01/oil-spill-tiyas

DeepLabV3+ Oil Spill Detection - Oil Spill Detection

Model Description

Model Details

Usage

Model Performance

Deployment

License

deytiyas01/
oil-spill-tiyas