The ResNet Family: What’s the Difference Between ResNet18, 34, 50, 101 - And When Do You Choose Each One?

ResNet isn’t a single model - it’s a family of networks designed for different tasks. The number in the model’s name indicates the total number of stages.

Why Are There Different Numbers?

Because different network lengths serve different purposes:

• ResNet18 - a short network. Very fast. Great for lightweight systems.
• ResNet34 - similar to 18 but with a bit more depth and accuracy.
• ResNet50 - a classic balance between depth and efficiency. The most widely used in industry.
• ResNet101 / 152 - very deep networks. Designed for complex tasks or large models.

The main difference is the amount of processing layers.

Why Did ResNet50 Become the Standard?

Because it combines:

• Meaningful depth
• Bottleneck Blocks (high efficiency)
• Excellent fit for a wide range of tasks
• Reasonable weight and good speed

That’s why it’s used as a Backbone in systems like YOLO, segmentation models, and more.

An Analogy

It’s like choosing a work team:

• 18 workers - a small, agile team, basic tasks.
• 34 workers - a bit more capability, still easy to coordinate.
• 50 workers - a strong team capable of taking on large projects.
• 101 workers - a huge team for especially heavy tasks.

Every project requires a different team size.

Conclusion

The ResNet family lets you choose the “depth level” that fits the task. That’s why it’s a central part of every modern vision system.

The next post will talk about integrating ResNet into large models - and how it serves as infrastructure for entire models.