Demonstration images can support discussion of visual objectives, file-reading checks or experiment preparation. Their existence alone is insufficient evidence that a complete training dataset has been delivered. Training suitability depends on the task, labels, coverage, data splits and the selected tool's input requirements.

Establish which labels the task needs

Image classification, object detection, semantic segmentation and character recognition can require different label forms. Class lists, class hints and scene names do not automatically provide bounding boxes, pixel annotations or character transcriptions. Even if a method does not require individual-image labels, check its training inputs and evaluation data against the method and business objective.

Check data and records before delivery

Check Information needed
Files and formats Inventory, readability, dimensions and data types
Labels and correspondence Types, rules, versions, valid scope and unconfirmed items
Origin and permissions Real or synthetic origin, permitted use and delivery scope
Coverage Classes, scenes, difficult conditions and gaps
Data splits Training, tuning, independent tests and related-sample handling
Quality checks Duplicates, corruption, exceptions and review records

Meeting a count does not complete performance verification

Meeting an agreed image count establishes that counting condition only. It is insufficient evidence of usable labels, successful training or verified performance at the actual deployment location. Keep synthetic-sample generation checks separate from independent target-model testing.

Delivering incomplete scope

Deliver demonstration examples, image-level packages or samples awaiting annotation under their completed scope. List missing labels, uncovered conditions and verification tasks. Unfinished work remains unfinished. Planned annotation, training or acceptance must not be presented as already passed.

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