AVIA · Data Curation
Find data problemsbefore training
Every sample is embedded into one space, where quality problems become visible: labels that disagree with their neighbors, near-duplicates, outliers, and regions your dataset barely covers.
Diagnostics
Four data problemsfound early
Suspected mislabels
Labels that disagree with their embedding neighbors are flagged for review.
Near-duplicates
Frames that add nothing new are thinned before they skew training.
Outliers
Corrupted, mis-shot, or out-of-distribution samples surface at the sparse edges.
Low coverage
Sparse regions show exactly which scenes and defect types are missing.
One space
See the wholedataset at once
Images and text map into one shared embedding space — search by phrase or reference image, and see clusters, gaps, and sparse edges directly.
- Search samples by text or by reference image
- Clusters and sparse edges are visible at a glance
- Diagnostics are drawn directly on the space
Active learning
Label the batchthe model needs most
Uncertainty and diversity scoring rank what's left unlabeled — so the batch you label next is the one the model needs most.
- Uncertainty surfaces what the model can't decide
- Diversity keeps batches from repeating themselves
- The hand-labeled share shrinks each round
How it runs
From embeddingto batch selection
Embed
Every sample maps into one searchable space.
Diagnose
Mislabels, duplicates, outliers, and gaps are flagged.
Review
People confirm or dismiss each flag.
Select
Active learning picks the next most valuable batch.
FAQ
Frequently askedquestions
Diagnostics run on embeddings, not on your model. Once samples are embedded into a shared space, labels that clash with their neighbors, near-duplicates, and outliers are geometric properties of that space.
Audit your datasetbefore training
Bring a dataset — we'll surface its mislabels, duplicates, and gaps in one pass.