Detection Keypoint Annotations (JSON)
Sample keypoint annotations (visible/occluded flags) for pose-estimation loader tests.
{
"image": "warehouse-scene.png",
"persons": [
{
"bbox": [
40,
80,
60,
50
],
"keypoints": [
70,
90,
2,
75,
110,
2,
65,
120,
1
]
}
]
}
Specifications
- Format
- COCO-style keypoints
- Persons
- 1
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Detection Keypoint Annotations (JSON)” 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: COCO-style keypoints. 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("warehouse-keypoints.json") as f:
data = json.load(f)
print(type(data), len(data))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.
- txtDetection 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.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.