Millistel juhtudel on soovitatav kasutada kahekordset detektorit tootmises?

In machine learning, a two-stage detector is commonly 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 is required: two-stage models, such as Faster R-CNN, first generate region proposals and then classify them, which improves detection quality.
• Restrictions on false positives: if it is important to minimize false positives, the two-stage approach helps better filter objects.
• Sufficient computational resources: two-stage detectors are generally 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, they are often combined with optimizations (e.g., quantization, pruning) and frameworks that support model export for efficient inference.