Detection Annotations — Aerial COCO (JSON)
COCO JSON with bounding boxes and polygon segmentation for the aerial scene.
{
"info": {
"description": "Novus Wave F aerial scene",
"version": "1.0"
},
"images": [
{
"id": 1,
"file_name": "aerial-scene.png",
"width": 320,
"height": 240
}
],
"categories": [
{
"id": 1,
"name": "building"
},
{
"id": 2,
"name": "road"
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [
50,
60,
90,
70
],
"area": 6300,
"iscrowd": 0,
"segmentation": [
[
50,
60,
140,
60,
140,
130,
50,
130
]
]
},
{Specifications
- Format
- COCO
- Annotations
- 2
- Segmentation
- true
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 Annotations — Aerial COCO (JSON)” is a deterministic Novus Examples fixture for Computer vision, ML training data, JSON parsing. 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. 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("aerial-coco.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- 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.
- jsonDetection Annotations — Retail COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the retail scene.
- jsonDetection Annotations — Warehouse COCO (JSON)COCO JSON with bounding boxes and polygon segmentation for the warehouse scene.
- jsonSemantic Segmentation Class Map (JSON)Class-id to name map for the semantic-segmentation mask (background + three shapes).
- jsonCaption Dataset Manifest (JSON)Manifest linking Wave F scene PNGs to per-language caption JSONL files.
- 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.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.