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Crop

Cropping is an image-processing operation that removes portions of an image to retain a selected subregion.

How 2 Manning books explain it

WordPress Power Toolkit: Harness AI to build next-level websites

A crop tool is an image-editing control for selecting and retaining part of an image while removing content outside the selection.

How it is used
Improves image composition by reframing scenes, strengthening focal points, and avoiding rigid symmetry.
How deep it goes
Defines cropping, explains rule-of-thirds rationale, and gives handle-dragging steps with iterative examples.
Examples
Uses cranes, a farmhouse landscape, and farmer portraits to compare focal-point placement and crop choices.
What you can do after
Apply it with a crop tool to position focal points, test aspect ratios, and select a balanced result.

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Inside Deep Learning: Math, Algorithms, Models

A preprocessing operation that extracts a spatial subregion from an image, here used to create training and test variants for shift analysis.

How it is used
Uses cropping to diversify training inputs, stabilize testing, and measure CNN sensitivity to spatial shifts.
How deep it goes
Explains crop size, random versus center policies, dataset wiring, and offset evaluation, but defers cropping internals.
Examples
Demonstrates 24 × 24 crops from 32 × 32 CIFAR-10 images and an 8 × 8 grid of shifted predictions.
What you can do after
Apply cropping via PyTorch transforms and diagnose shift sensitivity by comparing predictions across manually extracted windows.

Read how Inside Deep Learning: Math, Algorithms, Models explains crop →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.