Real-world computer vision (CV) deployment refers to taking a trained deep learning model and making it usable in real applications such as websites, mobile apps, or production systems. It bridges the gap between model training and real-world usage.<\/p>\n\n\n\n
What is CV Deployment?<\/strong> Why Deployment is Important<\/strong><\/p>\n\n\n\n Steps in Real-world CV Deployment<\/strong><\/p>\n\n\n\n 1. Train the Model<\/strong><\/p>\n\n\n\n 2. Save the Model<\/strong><\/p>\n\n\n\n 3. Choose Deployment Platform<\/strong><\/p>\n\n\n\n 4. Build API for Model<\/strong><\/p>\n\n\n\n 5. Connect Frontend<\/strong><\/p>\n\n\n\n 6. Test the System<\/strong><\/p>\n\n\n\n 7. Optimize for Production<\/strong><\/p>\n\n\n\n Deployment Example (Flask API)<\/strong><\/p>\n\n\n\n Tools for CV Deployment<\/strong><\/p>\n\n\n\n Challenges in Deployment<\/strong><\/p>\n\n\n\n Best Practices<\/strong><\/p>\n\n\n\n Applications<\/strong><\/p>\n\n\n\n Lesson Summary<\/strong>
Deployment is the process of integrating a trained computer vision model into an application where it can receive input data (images or video) and return predictions in real time or batch mode.<\/p>\n\n\n\n\n
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.h5<\/code>, .pt<\/code>, or SavedModel<\/li>\n<\/ul>\n\n\n\n\n
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from flask import Flask, request, jsonify
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Imageapp = Flask(__name__)model = load_model(\"model.h5\")@app.route('\/predict', methods=['POST'])
def predict():
image = request.files['image']
img = Image.open(image).resize((64, 64))
img_array = np.expand_dims(np.array(img) \/ 255.0, axis=0) prediction = model.predict(img_array)
return jsonify({\"prediction\": str(np.argmax(prediction))})if __name__ == \"__main__\":
app.run(debug=True)<\/pre>\n\n\n\n\n
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Real-world CV deployment transforms trained computer vision models into practical applications. By combining APIs, frontend systems, and optimized models, AI solutions can be used effectively in production environments.<\/p>\n\n\n