Checklist

Vision Inspection — Dataset & Labeling Checklist for Pilots

An interactive, practical checklist teams can complete and save to assess dataset readiness for machine-vision pilot projects. Collects key metrics, labeling rules, edge-case coverage, lighting/fixturing notes, class-balance indicators, and a structured pilot go/no‑go recommendation.

Interactive Tool

Vision Inspection — Dataset & Labeling Checklist for Pilots

Use this checklist to capture the facts and decisions that determine whether an image dataset gives a vision pilot a real chance to succeed. Complete the fields below, record counts and notes, and produce a clear go / no‑go recommendation for the pilot. Good answers reduce surprise from lighting, part variability, labeling ambiguity, and class imbalance.

How to use: Fill the key numeric fields and yes/no checks first, paste a short label list and sample-image links if available, then add notes where you see risk. If you use repeatable dataset naming conventions, enter them in Dataset name so future reports are consistent.

Short stable identifier for this dataset (e.g., LineA_2026-08-01_vision-pilot).
Person responsible for the pilot (name and email or extension).
Approximate dates when images were collected (e.g., 2026-06-01 to 2026-07-15).
Total number of labeled images intended for the pilot dataset. If images include video frames, enter frame count.
How many target classes are in the labeling schema (including 'no defect' or background classes).
Paste each label on its own line. Include any hierarchical notes (e.g., 'scratch > deep scratch').
A realistic minimum per-class sample target for the pilot (e.g., 50). Use a higher number for varied appearances.
Provide counts or a short table (label: count) for each class. If counts vary wildly, note the largest imbalances.
Rate how likely class imbalance will harm model training and evaluation (1 = low, 5 = severe).
1.0 10.0
Select the statement that best matches reality. Lighting is a very common source of failure for vision pilots.
Are parts held in a repeatable pose/position relative to the camera?
If there were multiple annotators, record how many and whether cross-checks were performed.
A short guideline that describes borderline cases, class definitions, and annotation policies reduces label noise.
Percent of samples cross-labeled by multiple annotators for quality control (e.g., 10). Leave blank if not done.
Have you identified and saved representative images of ambiguous or rare cases (yes/no)?
Paste links to a sample folder, S3 paths, or shared drive locations for quick review. Include a few representative filenames if possible.
Briefly describe likely sources of mistaken detections (e.g., reflective tape, shadows, new part variants).
Make a clear go / conditional / no‑go recommendation for starting the model pilot training and test runs.
Describe required fixes, expected timeline, and who will implement them. If 'Go', summarize why the dataset is sufficient.
Anything else the team should track before the pilot (e.g., sensor calibration, new fixturing plan, additional labeling budget).
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