The Fastest, Most Reliable COCO Evaluation Library for Computer Vision
| 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| Support & Development | Outdated and not actively maintained.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!
Get started in seconds with the core evaluation functionality:
pip install faster-coco-evalFor complete functionality including advanced visualization tools:
pip install faster-coco-eval[extra]If you use Anaconda/Miniconda:
conda install conda-forge::faster-coco-evalReplace 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.
Faster-COCO-Eval is built on top of a highly optimized C++ implementation, providing 3-4x faster evaluation than the standard pycocotools.
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!
See the performance in action:
Faster-COCO-Eval goes beyond basic evaluation with these advanced capabilities:
- 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
- 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
- PyTorch/TorchVision compatibility
- Seamless integration with mmdetection, Detectron2, and YOLO frameworks
- Distributed evaluation support
- Memory optimized for large datasets
- Boundary evaluation for segmentation tasks
- Custom dataset support
- Comprehensive API documentation
- Extensive test coverage and reliability
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
- Official Wiki - Complete API reference and guides
- Changelog - Latest updates and improvements
- API Documentation - Detailed function documentation
Faster-COCO-Eval is distributed under the Apache 2.0 license. See LICENSE for more information.
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}
}We welcome contributions! Check out our CONTRIBUTING.md for guidelines on how to get started.
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
pip install faster-coco-eval[extra]Replace pycocotools with Faster-COCO-Eval and experience evaluation at lightning speed!
