Faster-COCO-Eval

The Fastest, Most Reliable COCO Evaluation Library for Computer Vision

Replace pycocotools with Faster-COCO-Eval Today

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:

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!