Guus van der Ham
I’m an AI Solutions Engineer at Clockworks in Rotterdam. I studied AI and Data Science in Nijmegen, where I developed a passion for computer vision. Right after graduating, I joined Clockworks, where I’ve been building AI systems for real-world applications for the past four years.
Among other things, I’ve contributed to the Vision AI technology behind the Stack&Track solutions and our own product, Blicker. Through this work, I’ve seen how challenging it is to build systems that perform reliably outside controlled environments.
I’m especially interested in improving data quality, building better annotation workflows, and developing practical Vision AI systems that hold up in production.
Session
Your model isn't the problem. Your data is.
In production computer vision, teams consistently reach for model-level fixes, bigger backbones, longer training runs, more data, while the underlying dataset stays messy, mislabelled, and unrepresentative of the real world. We did the same, until we stopped.
This talk presents three data strategies we applied to a single, deceptively simple problem: counting crates moving through a logistics environment. The problem looked straightforward. The data was not.
The three strategies are:
- Annotation quality at scale: using model-assisted labelling and embedding-based clustering to systematically surface and correct errors hiding in production datasets
- Synthetic data generation: covering rare but critical edge cases absent from real captures, and navigating the domain gap between synthetic and real images
- Intelligent dataset reduction: selecting maximally informative training subsets, based on the counterintuitive principle that more data is not always better data
This is not a benchmarking talk. There are no leaderboard numbers here. The goal is to share a way of thinking about data that you can apply to your own problems, and to be honest about where each strategy works, where it struggles, and how they interact.
Target audience: Data scientists, ML engineers, and applied researchers working with real-world computer vision or ML pipelines.
Takeaways: A practical, transferable framework for approaching data quality.