---
id: faster-coco-eval
version: "1.7.2"
license: unclear
license_treatment: unclear
maintenance: active
---

# faster-coco-eval — Faster interpretation of the original COCOEval

License: unclear · Maintenance: active · Downloads: 681.3K/mo

## What it is and what it does

Faster-COCO-Eval is a drop-in replacement for COCO metric evaluation that accelerates computation through C++ optimizations. It computes standard object detection, instance segmentation, and keypoint detection metrics while maintaining a compatible API, so existing code can switch with a single initialization call. The package depends only on numpy and is distributed as prebuilt wheels for Python 3.7–3.13 across common platforms.

The library is actively maintained with recent commits and supports Python 3.7–3.13. It claims significant speedup on large datasets and includes support for extended metrics, alternative dataset formats, and visualization tools available via the [extra] install variant. The fact sheet records no known security vulnerabilities.

Use it for:

- Accelerate evaluation loops during model training or hyperparameter tuning when using COCO-format annotations.
- Replace existing COCO evaluation in detection pipelines without code changes.
- Evaluate instance segmentation or keypoint detection tasks where metric computation is a bottleneck.
- Generate error visualizations and metric analysis for model debugging.
- Run large-scale evaluations where computation time directly impacts iteration speed.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Faster-COCO-Eval provides a high-performance replacement for COCO metric evaluation that accelerates object detection, instance segmentation, and keypoint detection result computation through C++ optimizations while maintaining a compatible API.

Yes, with conditions. Install if you use COCO-format evaluation and want measurable speedup without rewriting code—the compatible API and prebuilt wheels make adoption low-friction. Verify the license from the repository first, especially in commercial contexts. The package is actively maintained, has no known vulnerabilities, and passes extensive test coverage, but the unclear license treatment requires a prerequisite check.

## Install

pip install faster-coco-eval
uv add faster-coco-eval
poetry add faster-coco-eval

## Installing faster-coco-eval

Before you install: Medium install friction due to platform-specific compiled wheels, but prebuilt binaries are available for Python 3.10–3.13 across macOS (ARM), Linux (x86_64, aarch64), and Windows (x64, ARM64). Maintenance is active with recent commits and no known vulnerabilities.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is recorded in the fact sheet. Before adopting this package in a commercial or copyleft-sensitive context, verify the actual license from the repository.

Quickstart:

pip install faster-coco-eval

import faster_coco_eval
faster_coco_eval.init_as_pycocotools()

# Now use standard COCO evaluation API
# Load annotations and predictions, then evaluate
val.evaluate()
val.accumulate()
val.summarize()

Requires numpy as a runtime dependency; prebuilt wheels exist for Python 3.7–3.13 on major platforms, but older or exotic architectures may require compilation.

Verify before relying:

- Exact performance improvement (3-4x claimed) on your specific hardware and dataset size.
- Whether the [extra] installation variant has additional dependencies beyond numpy.
- Compatibility with custom IoU types and alternative dataset formats in practice.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 681.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

coco evaluation faster, object detection metrics, instance segmentation eval, computer vision benchmarking, detection performance metrics, coco dataset evaluation, metric computation acceleration, computer-vision, benchmarking, performance-optimization

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