Artificial intelligence has revolutionized many industries, but when it comes to precise image recognition, it still lags behind specialized services. Let’s break down why.

1. The Problem of “Training on Generic Data”

Modern AI models (like GPT or Stable Diffusion) are trained on massive but heterogeneous datasets. However, real-world business tasks require recognizing highly specific objects:

• Defects on a production line
• Specific products among thousands of others
• Unique markers (QR codes, labels)

An AI not trained on specific examples will make mistakes.

2. Accuracy vs. “Guesswork”

Dedicated recognition services are fine-tuned for a single task, delivering 95%+ accuracy. AI, on the other hand, often follows a “looks similar, must be correct” approach – leading to errors.

3. Flexibility & Update Speed

If a client needs to recognize a new type of object, a specialized service can be retrained quickly. With AI, it’s more complicated: it requires vast amounts of new data, and even then, high accuracy isn’t guaranteed.

The Bottom Line

AI is a powerful tool for broad tasks, but when you need maximum precision in a niche field, dedicated recognition services remain the best choice.

Need a solution tailored to your needs? We develop recognition service that work exactly how you want.

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