We have seen a fixture change and a light do more for accuracy than a month of training would have
Sometimes the recommendation is to spend a small amount on hardware instead of a large amount on model work
That conversation happens in week one rather than after a disappointing pilot
Your defects are rare, which is good for the business and bad for training a classifier
Anomaly detection learns what an acceptable unit looks like and flags deviation, needing far fewer defect examples
Augmentation and synthetic generation close most of the rest
Bandwidth, latency and privacy usually rule out streaming video off site
We size models for the hardware on site, so only events and metrics travel
Throughput is a design input from the start, not a limit discovered during commissioning
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Yes, and that is the usual case. Collection and labelling get planned as part of the project. For rare defects we use anomaly detection and augmentation, which need far fewer positive examples than a classifier would.
Often. We check resolution, placement and lighting first, because those constrain accuracy more than model choice does. Where a camera cannot support the task we say what would need to change and roughly what it would cost.
Not if it cannot. Models run on edge devices or a local server, sending events and metrics rather than footage. For privacy-regulated environments that is usually the right architecture anyway.
We hold out a test set from your real footage, weighted toward the hard cases, and report per-class performance on it. If the model does not clear the agreed bar, it does not ship. That conversation happens before installation rather than after.
Seasonal light, new product variants and camera drift all degrade accuracy over time. We set up monitoring and a retraining path, and feed the misses into the next training round rather than treating the model as finished at launch.
