Multilingual Captions — Parallel (JSONL)
Parallel EN/ES/FR/DE captions per image for multilingual eval.
{"image": "warehouse-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
{"image": "retail-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
{"image": "aerial-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
{"image": "warehouse-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
{"image": "retail-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
{"image": "aerial-scene.png", "en": "Sample scene", "es": "Escena de muestra", "fr": "Scène d'exemple", "de": "Beispielszene"}
Specifications
- Records
- 6
- Languages
- 4
What is a .jsonl file?
JSONL (JSON Lines) is a text format where each line is a complete, independent JSON value, allowing records to be streamed and appended without parsing the whole file. It is not itself a JSON array and each line must stand alone. It is common in logging, machine learning datasets, and data pipelines.
How to use this file
Use an example JSONL to test line-by-line streaming parsers, append-and-resume ingestion, and batch pipelines that process one record per line.
How to use this file for testing
“Multilingual Captions — Parallel (JSONL)” is a deterministic Novus Examples fixture for Computer vision, ML training data. A rendered detection scene annotated in COCO, YOLO, and Pascal-VOC formats — for testing annotation loaders, format converters, and vision pipelines against a known image.
Documented properties for this file: 6 records. 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("captions-parallel.jsonl") as f:
rows = [json.loads(line) for line in f]
print(len(rows), rows[0])Related files
- jsonDetection Annotations — Aerial COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the aerial scene.
- jsonDetection Annotations — COCO (JSON)Object-detection annotations for the scene in the COCO JSON format — images, categories, and per-object bounding boxes as [x, y, width, height]. Grouped with YOLO and Pascal-VOC twins for testing annotation-format conversion.
- xmlDetection Annotations — Pascal VOC (XML)The same detection boxes in the Pascal VOC XML format — a per-image annotation with size, and one object element per box with pixel corner coordinates. The XML twin of the COCO and YOLO annotations.
- jsonDetection Annotations — Retail COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the retail scene.
- txtDetection Annotations — Retail YOLO (TXT)YOLO-format normalised boxes for the retail detection scene.
- jsonDetection Annotations — Warehouse COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the warehouse scene.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.