Build a Face Detection App with Python OOP — From Zero to Pro(part-2)

Part 2: ConfigModel — Centralized Model Management

Why We Need ConfigModel

YOLO models are heavy.

Reloading them inside multiple detectors would:

• Waste CPU/GPU
• Slow down FPS
• Increase RAM consumption
• Cause inconsistent model versions

So we implement ConfigModel, a class that loads models only once and stores them in a dictionary.

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How ConfigModel Works

✔ self.models = {}

Dictionary that keeps all loaded models.

✔ load_model(name, path)

Loads a model only if it hasn't been loaded before.

✔ get_model(name)

Retrieves the loaded model for use in detectors.

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Example Code (Your Structure Cleaned & Preserved)

class ConfigModel:
    def __init__(self):
        self.models = {}

    def load_model(self, model_name: str, model_path: str):
        if model_name not in self.models:
            from ultralytics import YOLO
            self.models[model_name] = YOLO(model_path)
        return self.models[model_name]

    def get_model(self, model_name: str):
        return self.models.get(model_name)

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Why This Architecture Is Good

✔ Prevents duplicate loading

YOLO loads once → all detectors reuse it.

✔ Scalable

You can add as many models as you want:

• YOLOv9-face
• YOLOv8-pose
• Segmentation models
• Custom detectors

✔ Centralized

All models live in one place → debugging becomes simple.

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Conclusion

ConfigModel is the foundation that keeps the entire system efficient, clean, and scalable.