Multi-Scale Testing is a test-time technique that runs inference at multiple input resolutions and combines the results — detecting objects or segmenting scenes more accurately by capturing features at different spatial scales.

How Does Multi-Scale Testing Work?

• Scales: Resize the input to multiple resolutions (e.g., 0.5×, 0.75×, 1.0×, 1.25×, 1.5×).
• Infer: Run the model at each scale independently.
• Combine: Average the predictions (for segmentation) or merge detections (NMS for detection).
• Optional: Combine with horizontal flipping for additional views.

Why It Matters

• Object Size Variation: Small objects are better detected at larger scales. Large objects at original scale.
• Segmentation: Multi-scale testing consistently improves mIoU by 1-3% on semantic segmentation benchmarks.
• Competitions: Standard practice in segmentation and detection competitions (but too slow for real-time).

Multi-Scale Testing is seeing at every zoom level — running inference at multiple resolutions to capture objects and details at all spatial scales.