Reports

Every completed validation run produces a report with scores across four dimensions, detailed findings in JSON, and optional annotated images highlighting detected issues.

Scoring

Reports score your dataset from 0 to 100 on each dimension, plus an overall composite score.

Overall

Weighted composite of all dimension scores.

Coverage

Scenario and class coverage completeness.

Physics

Physical plausibility of the synthetic data.

Distribution

Class balance and spatial distribution quality.

Sim-to-Real

Domain gap relative to real-world reference data.

Get a Report

Retrieve the full report for a completed validation run.

curl "https://api.lucitra.io/v1/reports/rpt_8abc1234" \
  -H "Authorization: Bearer luci_your_api_key"

import requests

resp = requests.get(
    "https://api.lucitra.io/v1/reports/rpt_8abc1234",
    headers={"Authorization": "Bearer luci_your_api_key"},
)
report = resp.json()

Response

{
  "id": "rpt_8abc1234",
  "run_id": "run_3fa85f64",
  "dataset_id": "ds_7kx9m2",
  "project_id": "proj_abc123",
  "overall_score": 82,
  "coverage_score": 91,
  "physics_score": 78,
  "distribution_score": 85,
  "sim_to_real_score": 74,
  "report_json": {
    "coverage": { "missing_classes": ["bicycle"], "edge_case_gaps": ["heavy_snow"] },
    "physics": { "violations": [{ "scene_id": "scene_042", "type": "intersection", "severity": "high" }] },
    "distribution": { "skewed_classes": ["pedestrian"], "gini_coefficient": 0.31 },
    "sim_to_real": { "fid_score": 42.7, "top_gaps": ["texture_quality", "shadow_direction"] }
  },
  "artifacts_path": "gs://lucitra-reports/proj_abc123/rpt_8abc1234/",
  "annotated_images": ["scene_042_physics", "scene_118_coverage"],
  "created_at": "2026-03-06T12:15:00Z"
}

string

required

Unique report identifier.

string

required

The validation run that produced this report.

string

required

The dataset that was validated.

string

required

The project this report belongs to.

integer

required

Composite score from 0 to 100.

integer

required

Coverage dimension score from 0 to 100.

integer

required

Physics plausibility score from 0 to 100.

integer

required

Distribution quality score from 0 to 100.

integer

Sim-to-real gap score from 0 to 100. Null if no reference dataset was provided.

object

required

Detailed findings for each validation dimension.

string

required

GCS path where report artifacts are stored.

array

required

List of image keys available for download via the images endpoint.

string

required

ISO 8601 timestamp of report generation.

Export as PDF

Download a formatted PDF version of the report suitable for sharing with stakeholders or attaching to compliance documentation.

curl "https://api.lucitra.io/v1/reports/rpt_8abc1234/pdf" \
  -H "Authorization: Bearer luci_your_api_key" \
  -o report.pdf

resp = requests.get(
    "https://api.lucitra.io/v1/reports/rpt_8abc1234/pdf",
    headers={"Authorization": "Bearer luci_your_api_key"},
)

with open("report.pdf", "wb") as f:
    f.write(resp.content)

The PDF endpoint returns a binary stream with Content-Type: application/pdf. Use the -o flag in cURL or write the response content as bytes in your client.

Annotated Images

Retrieve annotated images that highlight specific issues found during validation. The endpoint returns a 307 redirect to a time-limited signed GCS URL.

curl -L "https://api.lucitra.io/v1/reports/rpt_8abc1234/images/scene_042_physics" \
  -H "Authorization: Bearer luci_your_api_key" \
  -o annotated_scene_042.png

resp = requests.get(
    "https://api.lucitra.io/v1/reports/rpt_8abc1234/images/scene_042_physics",
    headers={"Authorization": "Bearer luci_your_api_key"},
    allow_redirects=True,
)

with open("annotated_scene_042.png", "wb") as f:
    f.write(resp.content)

The annotated_images array in the report response lists all available image keys. Use these keys in the URL path to download each image.

Score Trends

Track how validation scores change over time across multiple runs within a project. Use this to monitor data quality regressions or improvements across retraining cycles.

curl "https://api.lucitra.io/v1/projects/proj_abc123/trends?metric=overall&since=2026-01-01&until=2026-03-06" \
  -H "Authorization: Bearer luci_your_api_key"

resp = requests.get(
    "https://api.lucitra.io/v1/projects/proj_abc123/trends",
    headers={"Authorization": "Bearer luci_your_api_key"},
    params={
        "metric": "overall",
        "since": "2026-01-01",
        "until": "2026-03-06",
    },
)
trends = resp.json()

string

required

The score dimension to query. One of overall, coverage, physics, distribution, or sim_to_real.

string

required

Start date in YYYY-MM-DD format.

string

required

End date in YYYY-MM-DD format.

Response

{
  "data_points": [
    { "date": "2026-01-15", "score": 68, "run_id": "run_aaa111" },
    { "date": "2026-02-01", "score": 74, "run_id": "run_bbb222" },
    { "date": "2026-02-20", "score": 79, "run_id": "run_ccc333" },
    { "date": "2026-03-05", "score": 82, "run_id": "run_3fa85f64" }
  ]
}

array

required

Ordered list of score data points within the requested time range.

string

required

Date of the validation run.

integer

required

Score value for the requested metric (0-100).

string

required

The validation run that produced this data point.

Trends are available for all five metrics: overall, coverage, physics, distribution, and sim_to_real. Query each separately or build a multi-metric dashboard by making parallel requests.

Core

Integrations

Tools

Reports

Reports

Scoring

Overall

Coverage

Physics

Distribution

Sim-to-Real

Get a Report

Export as PDF

Annotated Images

Score Trends

​Reports

​Scoring

Overall

Coverage

Physics

Distribution

Sim-to-Real

​Get a Report

​Export as PDF

​Annotated Images

​Score Trends

Reports

Scoring

Get a Report

Export as PDF

Annotated Images

Score Trends