Detection Eval Metrics (JSON)
Synthetic mAP evaluation summary for object-detection benchmark harness tests.
{
"map50": 0.72,
"map50_95": 0.58,
"per_class": {
"box": 0.81,
"pallet": 0.69,
"forklift": 0.65
}
}
Specifications
- Metrics
- mAP
- Seed
- 314159
Testing contract
Expected to pass- Scenario
- Exercise Detection Eval Metrics (JSON) in its eval workflow. Synthetic mAP evaluation summary for object-detection benchmark harness tests.
- Expected result
- top-level keys are map50, map50_95, per_class; selected values: {"map50": 0.72, "map50_95": 0.58}. Declared feature checks: metrics=mAP.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Detection Eval Metrics (JSON)” is a deterministic Novus Examples fixture for Model evaluation, JSON parsing. Benchmark results, confusion matrices, ROC curves, and classification reports in CSV and JSON, for testing eval dashboards, metric parsers, and leaderboard importers.
Documented properties for this file: seed 314159. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas, no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
Code examples
import json
with open("detection-eval-metrics.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonClassification Report (JSON)A per-class classification report in the scikit-learn structure — precision, recall, F1, and support for each class plus accuracy and macro/weighted averages. A fixture for testing metric parsers and report renderers.
- jsonConfusion Matrix — 3 Classes (JSON)JSON twin of the 3-class confusion matrix.
- jsonConfusion Matrix — 3-class (JSON)The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
- jsonEval Metric — Accuracy MiniMinimal SAMPLE eval metric JSON (accuracy) for dashboard parsers.
- jsonEval Metric — Bleu MiniMinimal SAMPLE eval metric JSON (bleu) for dashboard parsers.
- jsonEval Metric — F1 MiniMinimal SAMPLE eval metric JSON (f1) for dashboard parsers.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.