Lucitra Validate — Synthetic Data Validation API

How it works

Four steps to validated training data

Upload

Push your synthetic dataset via API, CLI, or GitHub Action. Supports COCO, KITTI, USD, and custom formats.

Validate

Our models analyze coverage completeness, physics plausibility, distribution balance, and sim-to-real transfer quality.

Review

Get a structured report with per-category scores, severity-ranked violations, and specific improvement recommendations.

Improve

Fix the identified gaps, re-validate, and track your scores over time until the dataset is production-ready.

Detailed Reports

Real validation breakdown — Warehouse Robotics

Actual results from validating a 1,240-scene warehouse dataset rendered in Isaac Sim.

Coverage Breakdown

Object Distribution

38/52 object classes represented

Spatial Coverage

76% of navigable area covered

Viewpoint Diversity

8 camera angles, 2 heights

Scale Diversity

0.5x to 3x scale range

Physics Violations

ID	Description	Severity	Scenes
PV-001	Floating objects detected in 12 scenes	high	12
PV-002	Interpenetrating meshes in shelf region	medium	18
PV-003	Unrealistic shadow angles (light source mismatch)	medium	24
PV-004	Objects clipping through conveyor belt	high	8

Recommendations

Add 14 missing object classes: fire extinguisher, safety cone, pallet wrap, label printer, hand scanner, forklift variant B, caution sign, dock plate, shrink wrap, barcode reader, safety goggles, hard hat, high-vis vest, floor marking
Increase low-light scene variants (currently 4% of dataset, recommend 15-20%)
Fix floating object physics in warehouse aisle scenes — 12 scenes affected
Add forklift motion blur variants for dynamic scene coverage
Expand viewpoint diversity: add overhead and floor-level camera angles
Increase spatial coverage in loading dock and cold storage areas

Developer Experience

Integrate in minutes

.github/workflows/validate.yml

name: Validate Training Data
on:
  push:
    paths: ['data/**']
jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: lucitra/validate-action@v1
        with:
          api-key: ${{ secrets.LUCITRA_API_KEY }}
          dataset-path: ./data/latest.zip
          format: coco
          threshold: 80

validate.py

import lucitra
client = lucitra.Client(api_key="luci_...")
# Upload and validate
dataset = client.datasets.upload(
    "./warehouse-v3.zip",
    format="coco"
)
report = client.validate(
    dataset_id=dataset.id,
    type="full"
)
print(f"Score: {report.overall_score}")
print(f"Status: {report.status}")

Platform

Use it however you work

REST API

Full-featured API for uploading datasets, running validations, and retrieving reports programmatically.

Web Dashboard

Visual interface for browsing reports, comparing runs, and tracking dataset quality over time.

CLI

Command-line tool for scripting validations into your build pipeline. Install via npm.

MCP Server

Model Context Protocol server for integrating validation into AI-powered development workflows.

Start validating your synthetic data

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Validate synthetic training data via API