json1.5 KB

Detection Annotations — Warehouse COCO (JSON)

COCO JSON with bounding boxes and polygon segmentation for the warehouse scene.

Computer vision ML training data JSON parsing

Preview — first 50 linesjson

{
  "info": {
    "description": "Novus Wave F warehouse scene",
    "version": "1.0"
  },
  "images": [
    {
      "id": 1,
      "file_name": "warehouse-scene.png",
      "width": 320,
      "height": 240
    }
  ],
  "categories": [
    {
      "id": 1,
      "name": "box"
    },
    {
      "id": 2,
      "name": "pallet"
    },
    {
      "id": 3,
      "name": "forklift"
    }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 1,
      "category_id": 1,
      "bbox": [
        40,
        80,
        60,
        50
      ],
      "area": 3000,
      "iscrowd": 0,
      "segmentation": [
        [
          40,
          80,
          100,
          80,
          100,
          130,
          40,
          130

… 106 lines total — download for the full file.

Specifications

Format: COCO
Annotations: 3
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 — Warehouse 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("warehouse-coco.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.

Specifications

What is a .json file?

How to use this file

How to use this file for testing

Code examples

Related files