txt19 B

Detection Class List (TXT)

The class-name list for the detection scene, one label per line: index equals the zero-based line number, matching the YOLO class ids. A companion to the COCO/YOLO/VOC annotation files.

Computer vision ML training data

Preview, first 4 linestxt

person
car
tree

Specifications

Classes: 3
Order: person, car, tree
Note: index = line number (0-based)

Testing contract

Expected to pass

Scenario: Exercise Detection Class List (TXT) in its vision workflow. The class-name list for the detection scene, one label per line: index equals the zero-based line number, matching the YOLO class ids.
Expected result: 3 text lines, decoded as UTF-8; first nonempty line is 'person'. Declared feature checks: classes=3; order=person, car, tree.

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 Class List (TXT)” is a deterministic Testaroo 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: TXT · 19 bytes. 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.