Computer Vision Applications

Inspect resolution, colour channels and coordinate conventions.

Evidence: Image transforms preserve expected shapes and channel order.

Apply resize, normalisation and contrast adjustments.

Evidence: A comparison documents which transforms preserve task information.

Define consistent class and region labels with annotation rules.

Evidence: A reviewed sample resolves ambiguous examples consistently.

Apply training-only transformations suitable for the task.

Evidence: Validation images are untouched and unrealistic transforms are excluded.

Trace filters, activations and output shapes in a small model.

Evidence: A shape walkthrough and a reproducible baseline are submitted.

Train a classifier with a frozen backbone then limited fine-tuning.

Evidence: Experiments record data split, seed and training configuration.

Interpret boxes, confidence and overlapping detections.

Evidence: A demo produces valid boxes with correct coordinate conversion.

Compare pixel masks with bounding boxes for inspection.

Evidence: A mask metric is calculated on a small labelled example.

Measure classification or detection performance by failure type.

Evidence: A report includes false positives, missed defects and difficult conditions.

Compare batch size and image resolution on inference time.

Evidence: Measurements include accuracy impact and hardware details.

Test lighting changes and avoid unnecessary personal-image capture.

Evidence: A robustness table and data-minimisation plan are submitted.

Package a vision endpoint and visualise uncertain outputs.

Evidence: Tests reject invalid files and route low-confidence cases for review.