faster-coco-eval 1.7.2

Faster interpretation of the original COCOEval

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Support & Development	Outdated and not actively maintained.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.

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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:

• mmdetection comparison
• ultralytics comparison

🎯 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

✅ Testing & Reliability

Faster-COCO-Eval prioritizes correctness and reliability through extensive testing:

Comprehensive Test Suite

• 90+ automated tests covering all functionality
• Exact equality validation against pycocotools across all metrics
• Continuous integration on Python 3.9-3.13
• Edge case coverage including boundary conditions and error handling

Extensive PyCocoTools Comparison

New comprehensive tests validate exact numerical equality 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

All tests confirm bit-for-bit identical results between faster_coco_eval and pycocotools, giving you confidence to use this library as a drop-in replacement while gaining 3-4x performance improvements.

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:

1. Check the Wiki for common solutions
2. Search existing issues
3. 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!