VisionPack

DatasetOps for computer vision — a Git/Docker-like CLI for the messy part of training models: turning scattered images and labels into a clean, versioned, leak-free, ready-to-train dataset.

Get started Install View on GitHub

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pip install visionpack

vp init --name factory-defects --task detection
vp sync                          # pull images + labels from visionpack.yaml sources
vp validate                      # catch corrupt images, bad boxes, near-dup leakage
vp split create --strategy stratified
vp snapshot create -m "baseline"
vp export --format yolo --split  # ready-to-train layout

Why VisionPack

Most of the pain in a CV project isn’t the model — it’s the dataset. VisionPack targets the failures that quietly cost you accuracy and reproducibility:

• Train/test leakage that inflates your metrics. Exact-duplicate detection isn’t enough: a re-encoded or resized copy of a training image landing in the test set makes your reported numbers a lie. VisionPack catches near-duplicate leakage with perceptual hashing.
• Splits you can’t reproduce. “I shuffled with seed=42” breaks the moment data is added or reordered. VisionPack splits are a function of image content, so they’re identical across machines and stable as the dataset grows.
• Data scattered across places. Images in one bucket, labels in another repo, classes in a third. Declare them once and vp sync assembles the dataset — locally or straight from S3 / GCS / Azure.
• “Which dataset trained this model?” Content-addressed snapshots make that answerable instead of a guess.

It complements — not replaces — CVAT, FiftyOne, DVC, Roboflow, and Label Studio. VisionPack is the DatasetOps layer that imports, validates, versions, splits, packs, and exports.

Works across the common CV tasks

Task	Import	Geometry
Classification	ImageFolder (folder-per-class)	whole-image label
Detection	YOLO, COCO	bounding box
Instance segmentation	YOLO-seg, COCO	polygon
Semantic segmentation (export)	—	class-index mask PNGs (--format masks)
Keypoints / pose	COCO	keypoints

What’s in the box

• Deterministic, lockable splits — stratified / random / hash, captured in snapshots.
• Near-duplicate & cross-split leakage detection — perceptual-hash tier, scale-proof via LSH bucketing.
• Multi-source sync — declarative sources: + vp sync, with per-asset provenance.
• Cloud-native, multi-provider — sync YOLO/COCO/ImageFolder sources from and to S3/GCS/Azure without downloading the whole dataset, including cross-provider targets; see Cloud Sync.
• Content-addressed snapshots & diff — reproducible versions; compare any two.
• Strong validation — unreadable images, missing/orphan labels, unknown classes, bad boxes, exact + near duplicates, split leakage.
• Benchmarking & model-in-the-loop — vp eval (mAP / accuracy on a locked split), vp autolabel (confident predictions become labels), vp queue (what to label next).
• Packing & export — archive (.tar.zst) and training (WebDataset shards); byte-free exports via hardlinks / streaming manifests.
• Machine-readable output — every pipeline command takes --json and prints a stable, schema-versioned envelope; see JSON Output.

Next steps

• Install VisionPack — pip install visionpack
• Quickstart — a dataset in 60 seconds
• CLI Guide — every command and its options
• Cloud Sync — S3 / GCS / Azure datasets
• JSON Output — drive VisionPack from other programs

VisionPack is in active development (early but usable). The end-to-end workflow works across classification, detection, instance segmentation, and keypoints. APIs may still shift — feedback and contributions are welcome.