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
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.