Principle:Obss Sahi COCO FiftyOne Visualization
| Knowledge Sources | |
|---|---|
| Domains | Visualization, Evaluation, COCO |
| Last Updated | 2026-02-08 12:00 GMT |
Overview
Technique for importing COCO-formatted datasets and detection results into the FiftyOne platform for interactive visual inspection and error analysis.
Description
COCO-FiftyOne Visualization bridges COCO-formatted annotation and result files with the FiftyOne interactive exploration tool. It loads ground truth annotations as a FiftyOne dataset, overlays one or more detection result sets as separate label fields, runs evaluation metrics (with configurable IOU thresholds), and presents samples sorted by error counts (e.g., false positives). This enables visual debugging of detection model outputs directly in the browser.
Usage
Apply this principle when you need to visually inspect and compare detection model outputs against ground truth, particularly for identifying systematic error patterns like false positives in specific image regions or categories.
Theoretical Basis
The workflow follows three stages:
- Import: Convert COCO JSON → FiftyOne Dataset (preserving image paths and annotation structure)
- Overlay: Add detection result JSONs as separate prediction fields using COCO ID mapping
- Evaluate: Compute per-sample metrics (TP/FP/FN at given IOU) and sort by error count for targeted review