Use Case

Foundation Model Training

Foundation and vision-language models are only as capable as the data they learn from. Payana delivers large, diverse, accurately labeled multimodal datasets so your training runs start from clean, structured data instead of raw footage.

Web-scraped data is noisy, weakly labeled, and increasingly exhausted - the frontier of model quality has shifted to curated, richly annotated data. Payana produces exactly that: every second of footage becomes structured, machine-readable data carrying objects with bounding boxes and pixel-accurate masks, the action being performed, a natural-language scene description, the spoken transcript, and per-label confidence scores.

Annotation combines an AI pipeline with human judgment. Machine pre-labeling runs on GPU - detection models find every object and tool, segmentation traces exact boundaries, speech becomes text - and human reviewers then verify every image and episode in a purpose-built review workspace. Automatic curation flags off-task or low-quality segments before they ever enter a dataset, so what you train on is signal, not noise.

Because multimodal training runs are expensive, reproducibility matters. Every Payana dataset ships as an immutable, versioned release with fixed train/valid/test splits and quality metrics, so you always know exactly what a given checkpoint was trained on and can rerun or ablate experiments months later.

Scale comes from the capture side too: wearable-camera collection produces long, continuous recordings that our episode pipeline automatically segments into task-sized clips - turning hours of raw footage into thousands of clean training samples without manual cutting.

How it works

  1. 1

    Source

    Send us your images and video, or we run wearable-camera capture of the activities you care about.

  2. 2

    Machine pre-label

    A GPU pipeline annotates every second: objects, masks, actions, scene descriptions, and transcripts.

  3. 3

    Human review

    Reviewers verify and correct every label; automatic curation removes off-task or low-quality segments.

  4. 4

    Versioned delivery

    You receive an immutable dataset version with train/valid/test splits, quality metrics, and your chosen formats.

What you get

  • Multimodal labels: objects, boxes, masks, actions, scene descriptions, transcripts
  • Per-label confidence scores for filtering and curriculum design
  • Human review + automatic quality curation included
  • Immutable, versioned train/valid/test splits
  • Exports in COCO JSON, YOLO, Parquet and JSONL

Datasets export as COCO JSON for detection and segmentation research, YOLO for Ultralytics pipelines, and Parquet or JSONL for large-scale multimodal training loaders - each as a versioned release with fixed splits.

Frequently asked questions

Diversity, label richness, and cleanliness. A strong foundation-model dataset covers varied scenes and tasks, carries multiple aligned modalities per sample (visual labels, actions, text descriptions, transcripts), and has been curated so mislabeled or off-task samples don't poison training. Payana datasets are built to those three criteria: multimodal labels on every second, human review of every sample, and automatic curation before release.

Have footage to annotate?

Tell us your task and target format - we handle capture, annotation, review and delivery end to end.

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