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Support & Development	Outdated and not actively maintained.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.	3-4x faster due to C++ optimizations and modern algorithms.
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 `pip install`, no compilation required, and drop-in replacement API.
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 = 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 will run 3-4x faster with no changes.

⚡ Blazing Fast Performance

Faster-COCO-Eval is built on top of a highly optimized C++ implementation, providing 3-4x faster evaluation than the standard pycocotools.

Real-World Performance Benchmark

Tested on 5000 images from the COCO validation dataset using mmdetection framework:

Evaluation Type	Faster-COCO-Eval (sec)	pycocotools (sec)	Speedup
Bounding Boxes	5.812	22.72	3.9x
Segmentation	7.413	24.434	3.3x

For large datasets, this means hours saved on evaluation time!

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, Detectron2, 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

📚 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:

Check the Wiki for common solutions
Search existing issues
Open a new issue with detailed information about your problem

🚀 Get Started Today

pip install faster-coco-eval[extra]

Replace pycocotools with Faster-COCO-Eval and experience evaluation at lightning speed!