Choosing the problem type
Separating acceptable and unacceptable products can be framed as classification; predicting a numerical quantity as regression. State who creates labels and under which rule. If a simple rule meets the need, test it first as a baseline. Complexity alone does not create operational value.
Training and test boundaries
Training, validation and final test data serve different purposes. Similar views of the same item scattered across sets can make results look too good. For production imagery, consider grouping by item or capture session. Do not repeatedly use the final test to guide model and threshold selection.
Monitoring the cost of errors
Overall accuracy may be misleading when defects are rare. Assess missed defects and false rejections separately, and provide review for uncertain cases. Recheck new samples when products, cameras or processes change. Justify retraining using data changes and observed error types rather than a fixed promise of improvement.