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.
Code examples
import json
with open("annotations.coco.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- 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.
- jsonText Embeddings — 16-dim (JSON)A set of 24 L2-normalised 16-dimensional text embeddings as JSON — each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.
- txtDetection Class List (TXT)The class-name list for the detection scene, one label per line — index equals the zero-based line number, matching the YOLO class ids. A companion to the COCO/YOLO/VOC annotation files.
- jsonlChat Fine-tuning Dataset — Anthropic Format (JSONL)The same synthetic conversations in the Anthropic Messages JSONL shape — a top-level system prompt plus a messages array of user and assistant turns. The format twin of the OpenAI file, for testing chat-format conversion.
- jsonlChat Fine-tuning Dataset — OpenAI Format (JSONL)A chat fine-tuning dataset in the OpenAI JSONL format — one conversation per line as a messages array with system, user, and assistant turns. Synthetic Q&A content. Paired with an Anthropic-format twin for testing format converters.
- jsonConfusion Matrix — 3-class (JSON)The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.