LatestDataset Quality
What actually makes training data usable
Volume is the easiest property to measure and the least predictive of whether a dataset improves a model. Here is what tends to matter instead.
3 min read
Read the article
Insights
What we have learned about specification, annotation and quality — written for the people who have to make these decisions.
LatestDataset Quality
Volume is the easiest property to measure and the least predictive of whether a dataset improves a model. Here is what tends to matter instead.
3 min read
Read the article
8 articles
Two annotators disagreeing about the same example is not a training problem. It is a definitions problem, and it is far cheaper to solve before collection than after.
3 min read
Egocentric footage preserves attention, approach and sequence — spatial context that third-person capture quietly discards.
3 min read
Most annotator disagreement in video happens when an object is partly hidden. Deciding that once, in writing, is what keeps a track coherent.
3 min read
How you write numbers, hesitation and overlap is not a detail. It is a decision the entire corpus inherits, and changing it later means starting again.
3 min read
Persistent disagreement on the same kind of example is almost never carelessness. It is a label set that cannot be applied consistently.
2 min read
Generated data is genuinely useful for coverage and for privacy-constrained domains. It is not a substitute for the real-world anchor.
2 min read
The most common mistake is committing to volume before the specification has been tested. A small end-to-end pass costs less and tells you more.
3 min read
A single agreement score is a weak summary of a rich signal. What matters is where the disagreement sits and whether it is structured.
3 min read
Start a project
Tell us what you are building and what the model keeps getting wrong. That is usually enough to say what the dataset would have to cover.