In which cases is it worth applying a two-step detector in production?

A two-stage detector in machine learning is typically used in object detection tasks where high accuracy and reliability are required. In production, it is recommended to use it in the following cases:

• High detection accuracy required: two-stage models, such as Faster R-CNN, first generate region proposals and then classify them, which improves detection quality.
• Constraints on false positives: if minimizing false positives is important, the two-stage approach helps better filter objects.
• Sufficient computational resources: two-stage detectors are usually more resource-intensive, so they are used when infrastructure allows.
• Complex scenes with many objects: when objects overlap or have complex shapes, two-stage models show better results.

In production, two-stage detectors are often combined with optimizations (e.g., quantization, pruning) and use frameworks that support model export for efficient inference.