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Detection Annotations: YOLO (TXT)

The same detection boxes in the YOLO text format: one object per line as class id and box centre, width, and height normalised to 0–1. The format twin of the COCO and VOC annotations.

Computer visionML training dataConversion testing
Preview, first 4 linestxt
0 0.195312 0.625000 0.109375 0.375000
1 0.656250 0.750000 0.375000 0.250000
2 0.867188 0.520833 0.171875 0.666667

Specifications

Format
YOLO
Objects
3
Schema
class_id x_center y_center width height
Normalized
true

Testing contract

Expected to pass
Scenario
Exercise Detection Annotations: YOLO (TXT) in its vision workflow. The same detection boxes in the YOLO text format: one object per line as class id and box centre, width, and height normalised to 0–1.
Expected result
3 text lines, decoded as UTF-8; first nonempty line is '0 0.195312 0.625000 0.109375 0.375000'. Declared feature checks: objects=3; normalized=True.

What is a .txt file?

TXT is a plain-text file containing unformatted character data with no styling or structure beyond line breaks. Its interpretation depends on character encoding, most commonly UTF-8, and on line-ending convention. It is the most universal and portable text container.

How to use this file

Use an example TXT to test encoding detection, line-ending (LF versus CRLF) handling, and any tool that reads or streams raw text input.

How to use this file for testing

“Detection Annotations: YOLO (TXT)” is a deterministic Testaroo fixture for Computer vision, ML training data, Conversion testing. 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: schema: class_id x_center y_center width height · YOLO. 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.

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

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