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name: structured-datasets description: "Guide fastdup workflows for labeled image datasets, object detection annotations, dataset-source loaders, and export helpers." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

structured-datasets

Use this sub-skill when the workflow already has labels, splits, bounding boxes, or a notebook-guided dataset-source example.

Use when

The request mentions any of the following:

• image classification datasets with labels or train/test splits
• object detection datasets with COCO-style or bbox annotations
• annotation dataframes with filename, label, split, or bbox columns
• notebook-guided source examples for Hugging Face, Kaggle, Roboflow, Labelbox, TensorFlow Datasets, or Torchvision
• CVAT or LabelImg export helpers
• read_coco_labels, fd.run(annotations=...), or labeled gallery workflows

Typical workflow

1. Decide whether the input is a dataframe, a source adapter, or a generated fixture.
2. Normalize the dataframe columns before the run.
3. For object detection, keep the bbox columns consistent and choose data_type="bbox" when needed.
4. Run fd.run(annotations=...) and inspect the label-aware galleries.
5. Export to external tools only after the fastdup run succeeds.

What to read

• ../../references/api-reference.md for the fd.run(...) and gallery entry points
• ../../references/data-formats.md for annotation schemas and export-oriented outputs
• ../../references/workflows.md for the labeled-data workflow family
• ../../references/source-loaders.md for notebook-based dataset-source examples and their failure modes
• ../../references/exports.md for CVAT and LabelImg handoff details
• references/troubleshooting.md in this sub-skill for annotation and loader-specific issues
• ../../references/troubleshooting.md for package and platform-wide failures

Bundled scripts

Run these for reproducible local smoke checks:

• ../../scripts/make_synthetic_bbox_data.py — create a tiny bbox fixture and matching annotation tables
• ../../scripts/run_labeled_classification_smoke.py — exercise the labeled image workflow on synthetic data
• ../../scripts/run_labeled_detection_smoke.py — exercise the bbox workflow on synthetic data

Common decisions

• Prefer manual annotation dataframes for the most reliable path.
• Make sure the annotation dataframe matches the input folder layout.
• Use absolute filenames when the matching logic is ambiguous.
• For bbox work, preserve the coordinate column names expected by fastdup.
• Treat source-specific loaders as optional conveniences; they are not required for the core labeled-data workflow.
• Prefer the manual dataframe path when a source package, cache, or credentialed download is unstable.

Known limitation

The convenience Hugging Face dataset wrapper is currently fragile in the inspected release line and may fail during import. If it fails, fall back to a manually built dataframe rather than blocking the task.