jsonl569 B

Image Caption Dataset (JSONL)

Six synthetic image-caption pairs in JSON Lines — for testing captioning loaders and vision-language eval harnesses.

Computer vision ML training data NLP datasets

Preview — first 7 linesjsonl

{"id": "c1", "image": "fruit-still-life.png", "caption": "A fruit still life on a white background."}
{"id": "c2", "image": "coffee-mug.png", "caption": "A ceramic coffee mug centred on a desk."}
{"id": "c3", "image": "potted-plant.png", "caption": "A small potted plant with green leaves."}
{"id": "c4", "image": "colour-bars.png", "caption": "A grid of solid colour swatches."}
{"id": "c5", "image": "chart-bar.png", "caption": "A simple bar chart with labelled axes."}
{"id": "c6", "image": "invoice-scan.jpg", "caption": "A scanned sample invoice document."}

Specifications

Records: 6
Task: image captioning
Schema: id, image, caption

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

“Image Caption Dataset (JSONL)” is a deterministic Novus Examples fixture for Computer vision, ML training data, NLP datasets. 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 · schema: id, image, caption · task: image captioning. 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("image-captions.jsonl") as f:
    rows = [json.loads(line) for line in f]
print(len(rows), rows[0])

Generated by generation/ai_wave_b.py. Free for any use, no attribution required — license.