Sample position
This sample demonstrates data specification, baseline training, error analysis and deployment validation for object detection in experimental, engineering-inspection or instrument-acquired images. It does not use named client data or contain unauthorised performance results.
Input conditions
- Image source, resolution, acquisition conditions, authorisation scope and de-identification requirements;
- Target classes, annotation rules, ignore regions and difficult-sample definitions;
- Training, validation and test-split rules;
- Deployment device, inference interface, latency or resource constraints and maintenance method.
Technical route
- Check files, annotations, class distribution and duplicate samples; generate a data-quality record;
- Freeze the data version, random seed, configuration and base model to establish a reproducible baseline;
- Evaluate using pre-agreed metrics and thresholds while retaining per-sample results;
- Categorise missed detections, false detections and domain-shift samples to define the next data or algorithm action;
- Export the target runtime format and verify inputs, outputs, dependencies, resource use and exception handling.
Potential deliverables
- Data and annotation checking tools;
- Training, evaluation and inference code with configuration;
- Model files, interface documentation and deployment scripts;
- Metric records, error-sample inventory and reproduction notes.
Validation boundary
Any accuracy, recall, latency or resource conclusion applies only to the recorded data version, device and software environment. An actual project must define formulae, thresholds, sample scope and acceptance scripts in its technical appendix.