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Enhanced README with detailed benefits, installation options, performance benchmarks, and feature highlights #69

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# Faster-COCO-Eval

**The Fastest, Most Reliable COCO Evaluation Library for Computer Vision**

[![PyPI](https://img.shields.io/pypi/v/faster-coco-eval)](https://pypi.org/project/faster-coco-eval)
[![PyPI Downloads](https://img.shields.io/pypi/dm/faster-coco-eval.svg?label=PyPI%20downloads)](https://pypi.org/project/faster-coco-eval/)

[![Conda Version](https://img.shields.io/conda/vn/conda-forge/faster-coco-eval.svg)](https://anaconda.org/conda-forge/faster-coco-eval)
[![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/faster-coco-eval.svg)](https://anaconda.org/conda-forge/faster-coco-eval)

[![docs](https://img.shields.io/badge/docs-latest-blue)](https://github.com/MiXaiLL76/faster_coco_eval/wiki)
[![license](https://img.shields.io/github/license/MiXaiLL76/faster_coco_eval.svg)](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/LICENSE)

[![CI - Test](https://github.com/MiXaiLL76/faster_coco_eval/actions/workflows/unittest.yml/badge.svg)](https://github.com/MiXaiLL76/faster_coco_eval/actions/workflows/unittest.yml)

## Why should you replace pycocotools with **faster-coco-eval**?
## 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| **Support & Development** | Outdated and not actively maintained.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. | Several times faster due to C++ optimizations and modern algorithms. |
| **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. |

______________________________________________________________________
---

**By choosing faster-coco-eval, you benefit from:**
**Key Benefits of Faster-COCO-Eval:**

- Reliability and confidence in your results
- High processing speed
- Modern functionality and support for new tasks
- An active community and prompt response to your requests
✅ **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

Switch to **faster-coco-eval** and experience a new standard in working with COCO annotations!
**Join thousands of computer vision researchers and engineers who have already switched to Faster-COCO-Eval!**

## Install
## Quick Installation

### Basic implementation identical to pycocotools
### Option 1: Basic (Drop-in Replacement)

Get started in seconds with the core evaluation functionality:

```bash
pip install faster-coco-eval
```

### Additional visualization options
### Option 2: Full Installation (with Visualization)

> Only 1 additional package needed opencv-python-headless
For complete functionality including advanced visualization tools:

```bash
pip install faster-coco-eval[extra]
```

### Conda install
### Option 3: Conda Installation

If you use Anaconda/Miniconda:

```bash
conda install conda-forge::faster-coco-eval
```

### Basic usage
## 🚀 Quick Start: Drop-in Replacement

Replace pycocotools with Faster-COCO-Eval in **2 lines of code**:

```py
```python
import faster_coco_eval

# Replace pycocotools with 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

anno = COCO(str(anno_json)) # init annotations api
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
# 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()
```

## Faster-COCO-Eval base
**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:

This package wraps a facebook C++ implementation of COCO-eval operations found in the
[pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools) package.
This implementation greatly speeds up the evaluation time
for coco's AP metrics, especially when dealing with a high number of instances in an image.
| Evaluation Type | Faster-COCO-Eval (sec) | pycocotools (sec) | Speedup |
|-----------------|------------------------|-------------------|---------|
| Bounding Boxes | 5.812 | 22.72 | **3.9x** |
| Segmentation | 7.413 | 24.434 | **3.3x** |

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

For our use case with a test dataset of 5000 images from the coco val dataset.
Testing was carried out using the mmdetection framework and the eval_metric.py script. The indicators are presented below.
### Colab Examples

Visualization of testing **colab_example.ipynb** available in directory [examples/comparison](https://nbviewer.org/github/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison)
See the performance in action:

- [mmdet example](https://nbviewer.org/github/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/mmdet/colab_example.ipynb)
- [ultralytics example](https://nbviewer.org/github/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/ultralytics/colab_example.ipynb)
- [mmdetection comparison](https://nbviewer.org/github/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/mmdet/colab_example.ipynb)
- [ultralytics comparison](https://nbviewer.org/github/MiXaiLL76/faster_coco_eval/blob/main/examples/comparison/ultralytics/colab_example.ipynb)

### Summary for 5000 imgs
## 🎯 Powerful Features

| Type | faster-coco-eval | pycocotools | Profit |
| :--- | ---------------: | ----------: | -----: |
| bbox | 5.812 | 22.72 | 3.909 |
| segm | 7.413 | 24.434 | 3.296 |
Faster-COCO-Eval goes beyond basic evaluation with these advanced capabilities:

## Features
### 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

This library provides not only validation functions, but also error visualization functions. Including visualization of errors in the image.
You can study in more detail in the [examples](https://mixaill76.github.io/faster_coco_eval/examples.html) and [Wiki](https://github.com/MiXaiLL76/faster_coco_eval/wiki).
### 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**

## Usage
### Modern Integrations
- **PyTorch/TorchVision compatibility**
- **Seamless integration with mmdetection, Detectron2, and YOLO frameworks**
- **Distributed evaluation support**
- **Memory optimized for large datasets**

Code examples for using the library are available on the [Wiki](https://github.com/MiXaiLL76/faster_coco_eval/wiki)
### Additional Tools
- **Boundary evaluation for segmentation tasks**
- **Custom dataset support**
- **Comprehensive API documentation**
- **Extensive test coverage and reliability**

### Examples
## 📚 Comprehensive Documentation

- [Eval example](https://mixaill76.github.io/faster_coco_eval/examples/eval_example.html)
- [Curve example](https://mixaill76.github.io/faster_coco_eval/examples/curve_example.html)
- [LVIS example](https://mixaill76.github.io/faster_coco_eval/examples/lvis_example.html)
- [Crowdpose example](https://mixaill76.github.io/faster_coco_eval/examples/crowdpose_example.html)
- [Custom keypoints example](https://mixaill76.github.io/faster_coco_eval/examples/ced_example.html)
- [showAnns example](https://mixaill76.github.io/faster_coco_eval/examples/show_example.html)
### Usage Examples

## Update history
Explore practical, runnable examples in Jupyter notebooks:

Available via link [history.md](https://mixaill76.github.io/faster_coco_eval/history.html)
- [Basic Evaluation](https://mixaill76.github.io/faster_coco_eval/examples/eval_example.html) - Get started with COCO evaluation
- [Metric Curves](https://mixaill76.github.io/faster_coco_eval/examples/curve_example.html) - Precision-recall and metric visualization
- [LVIS Evaluation](https://mixaill76.github.io/faster_coco_eval/examples/lvis_example.html) - Large vocabulary instance segmentation
- [CrowdPose Evaluation](https://mixaill76.github.io/faster_coco_eval/examples/crowdpose_example.html) - Keypoint detection for crowded scenes
- [Custom Keypoints](https://mixaill76.github.io/faster_coco_eval/examples/ced_example.html) - Extend to custom keypoint datasets
- [Annotation Visualization](https://mixaill76.github.io/faster_coco_eval/examples/show_example.html) - Display and analyze annotations

## Star History
### Detailed Documentation

- [Official Wiki](https://github.com/MiXaiLL76/faster_coco_eval/wiki) - Complete API reference and guides
- [Changelog](https://mixaill76.github.io/faster_coco_eval/history.html) - Latest updates and improvements
- [API Documentation](https://github.com/MiXaiLL76/faster_coco_eval/wiki) - Detailed function documentation

## ⭐ Star History

[![Star History Chart](https://api.star-history.com/svg?repos=MiXaiLL76/faster_coco_eval&type=Date)](https://star-history.com/#MiXaiLL76/faster_coco_eval&Date)

## License
## 📄 License

The original module was licensed with apache 2, I will continue with the same license.
Distributed under the apache version 2.0 license, see [license](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/LICENSE) for more information.
Faster-COCO-Eval is distributed under the Apache 2.0 license. See [LICENSE](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/LICENSE) for more information.

## Citation
## 📚 Citation

If you use this benchmark in your research, please cite this project.
If you use Faster-COCO-Eval in your research, please cite:

```
```bibtex
@article{faster-coco-eval,
title = {{Faster-COCO-Eval}: Faster interpretation of the original COCOEval},
title = {{Faster-COCO-Eval}: Faster and Enhanced COCO Evaluation Library},
author = {MiXaiLL76},
year = {2024}
}
```

## 🤝 Contributing

We welcome contributions! Check out our [CONTRIBUTING.md](https://github.com/MiXaiLL76/faster_coco_eval/blob/main/CONTRIBUTING.md) for guidelines on how to get started.

## 🐛 Issues and Support

If you encounter any issues or have questions:

1. Check the [Wiki](https://github.com/MiXaiLL76/faster_coco_eval/wiki) for common solutions
2. Search existing [issues](https://github.com/MiXaiLL76/faster_coco_eval/issues)
3. Open a new issue with detailed information about your problem

## 🚀 Get Started Today

```bash
pip install faster-coco-eval[extra]
```

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