Principle:Obss Sahi COCO Dataset Loading
| Knowledge Sources | |
|---|---|
| Domains | Object_Detection, Data_Engineering, COCO_Format |
| Last Updated | 2026-02-08 12:00 GMT |
Overview
The process of parsing COCO-format annotation files into structured in-memory objects that enable programmatic access to images, annotations, and categories for dataset manipulation.
Description
COCO (Common Objects in Context) is the standard annotation format for object detection datasets. A COCO annotation file is a JSON document with three main sections: images (list of image metadata), annotations (list of object annotations with bounding boxes and segmentation masks), and categories (list of object class definitions).
Loading a COCO dataset involves:
- Parsing: Reading the JSON file and validating its structure
- Index building: Creating efficient mappings from image IDs to their annotation lists
- Object construction: Wrapping raw JSON dicts into typed objects (CocoImage, CocoAnnotation) that provide computed properties (area, bounding box conversions, etc.)
- Category mapping: Building a dict from category IDs to names, with optional remapping
This structured representation is the foundation for all downstream operations: slicing, merging, splitting, evaluation, and format conversion.
Usage
Use COCO dataset loading as the first step in any dataset manipulation workflow: dataset slicing for training, dataset merging, evaluation, or analysis. It is the entry point for the COCO Dataset Slicing workflow.
Theoretical Basis
The COCO JSON format follows this schema:
# COCO JSON structure (simplified)
{
"images": [
{"id": 1, "file_name": "image1.jpg", "height": 480, "width": 640}
],
"annotations": [
{"id": 1, "image_id": 1, "category_id": 1,
"bbox": [x, y, width, height],
"segmentation": [[x1,y1, x2,y2, ...]],
"area": float, "iscrowd": 0}
],
"categories": [
{"id": 1, "name": "person"}
]
}
The key indexing operation builds an O(1) lookup from image_id to annotations:
# Pseudocode for index building
image_id_to_annotations = defaultdict(list)
for annotation in coco_dict["annotations"]:
image_id_to_annotations[annotation["image_id"]].append(annotation)
This avoids repeated O(n) scans when iterating over images.