Model Fine-Tuning vs. Pre-trained Models

Computer vision models can be used off-the-shelf or customized through fine-tuning. Choosing the right path depends on your data, accuracy goals, and timeline.

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Table of contents

• What are pre-trained models?
• What is fine-tuning?
• Key differences between pre-trained and fine-tuned models
• Why does fine-tuning matter?
• Use cases for pre-trained vs. fine-tuned models
• Model lifecycle in BaaS platforms
• FAQs

What are pre-trained models?

Pre-trained models are AI models trained on large, general-purpose datasets like ImageNet, COCO, or Open Images.

Benefits

• Ready to use immediately.
• No need for large datasets or heavy compute.
• Great for prototyping or broad tasks (object detection, classification).

Limitations

• May not perform well on domain-specific data.
• Biases from the training dataset may affect results.

What is fine-tuning?

Fine-tuning starts with a pre-trained model and adapts it to your domain by training on a smaller, task-specific dataset.

Benefits

• Higher accuracy on specialized use cases.
• Less data required compared to training from scratch.
• Leverages transfer learning to save time and cost.

Limitations

• Requires curated labeled data.
• Needs additional training infrastructure.
• Risk of overfitting if dataset is too small.

Key differences between pre-trained and fine-tuned models

• Speed to deploy: Pre-trained models are instant; fine-tuning adds training steps.
• Accuracy: Fine-tuned models excel in niche domains.
• Data requirements: Pre-trained requires none; fine-tuning requires labeled samples.
• Flexibility: Fine-tuning customizes for unique tasks; pre-trained is general-purpose.

Why does fine-tuning matter?

Many industries have edge cases that general-purpose models miss:

• A pre-trained defect detection model may flag scratches, but not your specific product flaws.
• A general medical imaging model may recognize lungs, but not subtle markers in a specialized scan.

Fine-tuning bridges the gap between generic intelligence and domain expertise.

Use cases for pre-trained vs. fine-tuned models

• Pre-trained:
  • Quick POC or MVP builds.
  • Standard use cases (face detection, object classification).
  • Applications where “good enough” accuracy works.
• Fine-tuned:
  • Medical imaging with unique scan types.
  • Retail SKU-level product recognition.
  • Industrial defect detection on specific machinery.
  • Security systems trained on environment-specific data.

Model lifecycle in BaaS platforms

Platforms like Lid Vizion support both paths:

• Deploy pre-trained models instantly for rapid testing.
• Fine-tune models on your labeled data using integrated workflows.
• Version and monitor models across environments.
• Roll out updates seamlessly from training to inference pipelines.

This flexibility helps teams evolve from prototype → production → continuous improvement.

FAQs

Do I always need to fine-tune?
Not always. If pre-trained models meet accuracy needs, use them as-is.

How much data is needed for fine-tuning?
Often hundreds to thousands of labeled samples, depending on complexity.

Can I fine-tune without GPUs?
You can with small datasets, but GPUs or cloud training environments accelerate the process.

Is fine-tuning the same as retraining?
No. Retraining starts from scratch; fine-tuning adapts an existing model.

Can I combine both approaches?
Yes. Many teams start with pre-trained, fine-tune later, and keep both versions available.

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