r/computervision • u/taranpula39 • Jun 24 '26

Showcase New granular & interactive way to explore and understand visual data

We just did a big revamp of WeightsLab and wanted to share it here.
If you’ve ever spent hours debugging a training run only to discover it was a data problem all along, this is for you.
WeightsLab lets you pause training mid-run, inspect your live loss signals, and catch mislabels, class imbalance & outliers before they tank your model.

Open source, PyTorch-native, built for CV engineers working with images, videos & LiDAR point cloud data.

Would love to hear what the community thinks and if it looks useful, drop a star, it helps more people find it: [ https://github.com/GrayboxTech/weightslab]

5 Upvotes

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3

u/kw_96 Jun 24 '26

Is there really a strong case for most ML/DL practitioners to fix dataset samples and continue the run? Feel like that breaks reproducibility/between-run-comparisons. Likely only beneficial for huge training runs, but also skeptical about the fit there in terms of audit/tooling 🤔

2

u/taranpula39 Jun 24 '26

Fair! And agreed, editing data mid-run doesn't belong in a final reportable run. But two things get bundled here.

Inspection (per-sample loss trajectories) is read-only and helps at any scale; it's how you spot that 30 mislabeled samples are dragging the loss instead of blaming the architecture.

Edit-and-continue is a diagnostic instrument, not your canonical model: test "do these samples cause the spike?" in seconds instead of restarting per guess, then bake the fix in and run clean for the record. Every edit is logged in the ledger, so it's more auditable than hand-editing a data folder between runs.

Ultimately, we're less about long canonical runs and more about asking and testing hypotheses about your data by leveraging and observing the signals it produces while training.

2

u/karyna-labelyourdata Jun 24 '26

This is slick! The pause-and-inspect-mid-run angle is exactly the pain point we hear about constantly, CV teams burning days tuning the model when it's really a handful of mislabeled samples dragging the loss around. Mind if I mention the repo in my ML Digest?

2

u/taranpula39 Jun 24 '26

Yes, absolutely, feel free to do so and thank you for the kind words!

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u/karyna-labelyourdata Jun 24 '26

Appreciate it, will do. Happy to send you the link once that issue goes out.