---

name: fastdup description: "Route fastdup workflows for visual dataset curation, annotation-driven analysis, embeddings and search, and video or exchange plumbing." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

fastdup

fastdup helps inspect visual datasets, surface duplicates and outliers, work with labeled image and object-detection data, reuse feature vectors, and generate galleries or exports from a completed run.

Use this repo skill when the task names fastdup directly or when the user asks about:

• duplicate, near-duplicate, outlier, broken-image, or mislabel discovery
• image-folder cleanup and connected-component summaries
• annotation-backed image classification or object detection workflows
• precomputed feature vectors, search, captions, OCR, or zero-shot enrichment
• video inputs, archives, cloud paths, or export helpers for CVAT / LabelImg

Start here

1. Confirm the workflow family and open the matching sub-skill.
2. Read references/data-formats.md if you need filenames, dataframe columns, or output files.
3. Read references/api-reference.md for exact entry points and gallery helpers.
4. Read references/troubleshooting.md when the run depends on a platform package, optional model, or tricky input layout.
5. If the workflow is source-loader or export heavy, also read references/source-loaders.md or references/exports.md.
6. If you need a smoke helper, run the bundled script that matches the workflow family.

Baseline usage

• Install the package with pip install fastdup.
• Verify the import with python -c "import fastdup; print(fastdup.__version__)".
• Add workflow-specific extras only when the selected sub-skill needs them.
• The common object flow is fd = fastdup.create(...), fd.run(...), then fd.vis.*.
• The one-shot flow is fastdup.run(...).
• The feature-vector flow uses fastdup.save_binary_feature(...), fastdup.load_binary_feature(...), and fastdup.init_search(...).

Sub-skills

dataset-curation

Use for local visual cleanup, duplicate removal, outliers, connected components, gallery generation, and binary feature round-trips.

Read:

• sub-skills/dataset-curation/SKILL.md
• references/workflows.md
• references/data-formats.md
• references/troubleshooting.md

Run:

• scripts/run_core_analysis_smoke.py
• scripts/run_feature_vector_smoke.py
• scripts/make_synthetic_image_data.py

structured-datasets

Use for labeled image classification, object detection, annotation dataframes, source loaders, and annotation-oriented exports.

Read:

• sub-skills/structured-datasets/SKILL.md
• references/workflows.md
• references/data-formats.md
• references/source-loaders.md
• references/exports.md
• references/troubleshooting.md for the known HF helper issue

Run:

• scripts/run_labeled_classification_smoke.py
• scripts/run_labeled_detection_smoke.py
• scripts/make_synthetic_bbox_data.py
• scripts/export_cvat_smoke.py
• scripts/export_labelimg_smoke.py

model-enrichment

Use for embeddings, precomputed vectors, image search, captions, OCR, and zero-shot enrichment.

Read:

• sub-skills/model-enrichment/SKILL.md
• references/api-reference.md
• references/workflows.md
• references/tensorboard-projector.md

Run:

• scripts/run_feature_vector_smoke.py
• scripts/run_search_smoke.py
• scripts/export_tensorboard_projector_smoke.py

media-and-exchange

Use for video inputs, tar/zip/webdataset plumbing, cloud path handling, and export helpers.

Read:

• sub-skills/media-and-exchange/SKILL.md
• references/workflows.md
• references/exports.md
• references/troubleshooting.md

Run:

• scripts/export_cvat_smoke.py
• scripts/export_labelimg_smoke.py

Routing rule of thumb

• Start with dataset-curation when the task is about image quality or duplicate removal.
• Start with structured-datasets when the task already has annotations or a dataset source adapter.
• Start with model-enrichment when the task mentions feature vectors, captions, OCR, or search.
• Start with media-and-exchange when the task involves videos, archives, cloud paths, or export plumbing.

Public metadata and staleness

• references/repo-provenance.md records the source commit, branch, dirty state, and evidence paths.
• references/repo-routing-metadata.json feeds the repo-skills router during import.
• If the package version or API surface changes, refresh this skill before relying on it for new work.