Multi-crop testing is the evaluation method that runs inference on several spatial crops of the same image and combines predictions to reduce framing bias - this is especially useful when important objects are not centered or occupy only a small image region.

What Is Multi-Crop Testing?

• Definition: Inference over a predefined set of crops, often center plus four corners, followed by prediction averaging.
• Purpose: Ensure model sees alternative spatial contexts that one center crop may miss.
• Common Setup: Five-crop or ten-crop protocol depending on benchmark strictness.
• Output Fusion: Mean logits or probabilities across crop predictions.

Why Multi-Crop Testing Matters

• Coverage: Captures objects near edges that center crop can truncate.
• Accuracy Gain: Often provides incremental but reliable metric improvement.
• Evaluation Fairness: Reduces dependence on a single crop convention.
• Model Diagnostics: Reveals sensitivity to object position and framing.
• Deployment Option: Can be enabled for high confidence applications.

Crop Protocols

Five-Crop:

• Four corners plus center.
• Balanced cost and benefit.

Ten-Crop:

• Five-crop plus horizontal flips.
• Higher accuracy at higher compute cost.

Adaptive Crop:

• Generate crops based on saliency or detector proposals.
• Useful for objects with uncertain location.

How It Works

Step 1: Generate crop set from input image at chosen scale and run each crop through the model.

Step 2: Average predictions and output final class distribution, optionally with uncertainty score from crop variance.

Tools & Platforms

• torchvision transforms: Built in five-crop and ten-crop utilities.
• timm eval scripts: Support multi-crop validation out of the box.
• Inference services: Batch crops together to reduce latency overhead.

Multi-crop testing is a simple evaluation ensemble that improves spatial robustness by checking multiple viewpoints of the same image - it is an effective option when slight extra inference cost is acceptable.