Detection 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.
{
"info": {
"description": "Novus Examples synthetic detection scene",
"version": "1.0",
"year": 2026
},
"images": [
{
"id": 1,
"file_name": "street-scene.png",
"width": 640,
"height": 480
}
],
"categories": [
{
"id": 1,
"name": "person"
},
{
"id": 2,
"name": "car"
},
{
"id": 3,
"name": "tree"
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [
90,
210,
70,
180
],
"area": 12600,
"iscrowd": 0
},
{
"id": 2,
"image_id": 1,
"category_id": 2,
"bbox": [
300,
300,
240,Specifications
- Format
- COCO detection
- Images
- 1
- Annotations
- 3
- Bbox
- [x, y, width, height]
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 — COCO (JSON)” is a deterministic Novus Examples fixture for Computer vision, ML training data, JSON parsing, 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: COCO detection. 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("annotations.coco.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 — 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.
- 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.
- jsonSemantic Segmentation Class Map (JSON)Class-id to name map for the semantic-segmentation mask (background + three shapes).
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.