Data-centric debugging for teams training neural nets [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
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, and helps more people find it: [ https://github.com/GrayboxTech/weightslab]
[link] [comments]
More from r/MachineLearning
-
For the people who got reviews back from neurips, cvpr, eccv, etc and also tested their paper through an agentic reviewer like the stanford one, how different were the reviews? [D]
Aug 14
-
Building text to ASCII diffusion model , need advice and guidance [P]
Aug 14
-
A collision-entropy floor for watermark/retrieval AI-text detection. Looking for a sanity check before I take this further [D]
Aug 14
-
Are supervised and unsupervised learning still relevant today? [D]
Aug 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.