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A dataset in Blindsight is a versioned collection of files (images, text, or 3D volumes) plus its metadata and full lineage. Every scan, every healing run, and every compliance report ties back to a specific dataset version.

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Creating a dataset

From Datasets → New, pick a source. Each source captures credentials once and remembers them for next time.
Blindsight does Zip Slip protection on extraction, so a malformed archive cannot escape the dataset directory.

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Dataset detail page (/datasets/:id)

The dataset page is your home base for one piece of data.
Click any thumbnail in the preview grid to open a full‑size viewer with class metadata, EXIF, and the list of every scan that has touched that file. Useful while triaging a single weird sample.

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Branches

Branches let you maintain alternative versions of the same dataset without physically copying files. Each branch is a manifest CSV that declares which files are in or out, plus any label overrides. Typical uses:
  • A clean branch after healing, so you can A/B train on cured vs raw data without losing the original.
  • A balanced branch with a downsampled subset for fast iteration.
  • An experimental branch with tweaked labels for an A/B scan.
Switching branches on the dataset detail page instantly changes which files are used in the next scan. Every branch keeps its own scan history.
1

Create a branch

From the Branches tab, click New branch, give it a name, optionally describe how it differs from the main branch.
2

Define what's in it

Either upload a manifest CSV (path,split,label,include) or use the inline editor to flip the include flag on individual files.
3

Run a scan against it

The branch shows as a selector on the New scan screen. Pick it and Blindsight scans only the branch’s manifest.
4

Promote or discard

Branches can be promoted to become the dataset’s main view, or deleted without affecting any other branch.

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Lineage

The lineage view is a directed graph showing where a dataset came from and what was produced from it.
  • Healing produces a child dataset with a healed_from edge.
  • Branching produces a child with a branched_from edge.
  • Playground publishes produce a child with a playground_session edge.
  • Imports keep a reference to the original source_uri.
Auditors typically ask “where did this training corpus come from?” and lineage is the single answer. Compliance reports include the same graph automatically.

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What to expect

  • Dataset uploads under ~5 GB usually appear within seconds.
  • Larger uploads stream progress via the WebSocket and resume cleanly if the network drops.
  • Statistics (valid_images, invalid_images, classes, class_count) populate as soon as ingestion finishes; you don’t have to wait for a scan to see them.