Faster-COCO-Eval
The Fastest, Most Reliable COCO Evaluation Library for Computer Vision
Replace pycocotools with Faster-COCO-Eval Today
Aspect |
pycocotools |
faster-coco-eval |
|---|---|---|
Support & Development |
Outdated and not actively maintained. Issues and incompatibilities arise with new releases. |
Actively maintained, continuously evolving, and regularly updated with new features and bug fixes. |
Transparency & Reliability |
Lacks comprehensive testing, making updates risky and results less predictable. |
Emphasizes extensive test coverage and code quality, ensuring trustworthy and reliable results. |
Performance |
Significantly slower, especially on large datasets or distributed workloads. |
Native C++ implementation with measured speedups that depend on the workload and hardware. |
Functionality |
Limited to basic COCO format evaluation. |
Offers extended metrics, support for new IoU types, compatibility with more datasets (e.g., CrowdPose, LVIS), advanced visualizations, and seamless integration with PyTorch/TorchVision. |
Ease of Use |
Requires manual installation, often with compilation issues. |
Simple |
Visualization |
Basic plotting capabilities. |
Advanced error visualization, annotation display, and comprehensive metric analysis tools. |
Key Benefits of Faster-COCO-Eval:
✅ Blazing Fast Performance - Evaluate large datasets in minutes instead of hours ✅ Reliable & Trusted - Extensive test coverage ensures consistent, reproducible results ✅ Modern Features - Support for latest CV tasks, IoU types, and dataset formats ✅ Easy to Use - Drop-in replacement for pycocotools with enhanced API ✅ Comprehensive Visualization - Understand your model’s performance with beautiful, informative plots
Join thousands of computer vision researchers and engineers who have already switched to Faster-COCO-Eval!
Quick Installation
Option 1: Basic (Drop-in Replacement)
Get started in seconds with the core evaluation functionality:
pip install faster-coco-eval
Option 2: Full Installation (with Visualization)
For complete functionality including advanced visualization tools:
pip install faster-coco-eval[extra]
Option 3: Conda Installation
If you use Anaconda/Miniconda:
conda install conda-forge::faster-coco-eval
🚀 Quick Start: Drop-in Replacement
Replace pycocotools with Faster-COCO-Eval in 2 lines of code:
import faster_coco_eval
# This single line replaces pycocotools with faster-coco-eval
faster_coco_eval.init_as_pycocotools()
# Now use the familiar pycocotools API
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# Load annotations and predictions
anno_json = "path/to/annotations.json"
pred_json = "path/to/predictions.json"
anno = COCO(str(anno_json)) # Annotations file
pred = anno.loadRes(str(pred_json)) # Predictions file
# Evaluate bounding boxes
val = COCOeval(anno, pred, "bbox")
val.evaluate()
val.accumulate()
val.summarize()
# Or evaluate segmentation masks
val = COCOeval(anno, pred, "segm")
val.evaluate()
val.accumulate()
val.summarize()
That’s it! Your existing code runs through the native evaluator with no API changes.
⚡ Blazing Fast Performance
Faster-COCO-Eval uses a native C++ implementation. The measured result below is one local reference point; real-world speedup varies with dataset size, metric, and hardware.
Real-World Performance Benchmark
Local reference measurement: 100 synthetic images, 15 ground-truth boxes and 100 detections per image, five categories, bbox evaluation, seven timed samples after two warmups. The measurement was run on 2026-08-23 on macOS 26.6.1 arm64, Python 3.10.11, and NumPy 2.2.6. The implementations’ eval["precision"] arrays matched within the comparison tolerance.
Evaluation Type |
Faster-COCO-Eval (sec) |
pycocotools (sec) |
Speedup |
|---|---|---|---|
Bounding Boxes |
0.053977 (MAD 0.001883) |
0.296960 (MAD 0.001667) |
5.50x |
This local measurement is hardware- and workload-dependent; it is not a universal performance guarantee.
Colab Examples
See the performance in action:
🎯 Powerful Features
Faster-COCO-Eval goes beyond basic evaluation with these advanced capabilities:
Core Evaluation
Drop-in pycocotools replacement - No code changes needed
Support for all COCO metric types: bbox, segm, keypoints
LVIS (Large Vocabulary Instance Segmentation) evaluation
CrowdPose and custom keypoint datasets
Multiple IoU types: standard, rotated, and custom IoU definitions
Advanced Visualization
Error visualization: See where your model is making mistakes
Annotation display: Visualize ground truth and predictions together
Metric curves: Precision-recall curves, class-wise performance
Confusion matrices and error analysis
Interactive Jupyter notebook examples
Modern Integrations
PyTorch/TorchVision compatibility
Seamless integration with mmdetection and YOLO frameworks
Distributed evaluation support
Memory optimized for large datasets
Additional Tools
Boundary evaluation for segmentation tasks
Custom dataset support
Comprehensive API documentation
Extensive test coverage and reliability
✅ Testing & Reliability
Faster-COCO-Eval prioritizes correctness and reliability through extensive testing:
Comprehensive Test Suite
90+ automated tests covering all functionality
Numerical parity checks against pycocotools across the supported metrics
Continuous integration on Python 3.10-3.13
Edge case coverage including boundary conditions and error handling
Extensive PyCocoTools Comparison
New comprehensive tests validate numerical parity within a strict tolerance with pycocotools:
Object Detection: Tests with 10-100 images, hundreds to thousands of annotations
Instance Segmentation: RLE mask encoding and pixel-level IoU validation
Keypoint Detection: 17-keypoint pose estimation with varied visibility
Multiple Scenarios: Small/medium/large objects, various confidence distributions
Edge Cases: Perfect predictions, low-confidence detections, mixed object sizes
The comparison suite checks numerical parity between faster_coco_eval and pycocotools, giving you confidence to use this library as a drop-in replacement. See the benchmark above for one measured bbox workload; it is not a universal performance guarantee.
See tests/README.md for detailed test documentation.
📚 Comprehensive Documentation
Usage Examples
Explore practical, runnable examples in Jupyter notebooks:
Basic Evaluation - Get started with COCO evaluation
Metric Curves - Precision-recall and metric visualization
LVIS Evaluation - Large vocabulary instance segmentation
CrowdPose Evaluation - Keypoint detection for crowded scenes
Custom Keypoints - Extend to custom keypoint datasets
Annotation Visualization - Display and analyze annotations
Detailed Documentation
Official Wiki - Complete API reference and guides
Changelog - Latest updates and improvements
API Documentation - Detailed function documentation
⭐ Star History
📄 License
Faster-COCO-Eval is distributed under the Apache 2.0 license. See LICENSE for more information.
📚 Citation
If you use Faster-COCO-Eval in your research, please cite:
@article{faster-coco-eval,
title = {{Faster-COCO-Eval}: Faster and Enhanced COCO Evaluation Library},
author = {MiXaiLL76},
year = {2024}
}
🤝 Contributing
We welcome contributions! Check out our CONTRIBUTING.md for guidelines on how to get started.
🐛 Issues and Support
If you encounter any issues or have questions:
🚀 Get Started Today
pip install faster-coco-eval[extra]
Replace pycocotools with Faster-COCO-Eval and experience evaluation at lightning speed!