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.
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
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 Novus Examples 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.
Related files
- xmlDetection Annotations — Warehouse Pascal VOC (XML)Pascal VOC XML annotations for the warehouse detection scene.
- pngObject-detection Scene (PNG, 640×480)A simple rendered street scene with a person, a car, and a tree at known pixel coordinates — the image the COCO, YOLO, and Pascal-VOC annotation twins describe. A fixture for testing object-detection loaders and annotation converters.
- jsonDetection Annotations — Aerial COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the aerial scene.
- 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_datasets.py. Free for any use, no attribution required — license.