faster-coco-eval 1.5.1

Faster interpretation of the original COCOEval

Faster-COCO-Eval

Disclaimer

I often use this project, but I saw it abandoned and without a public repository on github. Also, part of the project remained unfinished for a long time. I implemented some of the author's ideas and decided to make the results publicly available.

Faster-COCO-Eval base

This package wraps a facebook C++ implementation of COCO-eval operations found in the 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.

Comparison

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.

Visualization of testing comparison.ipynb available in directory examples/comparison Tested with yolo3 model (bbox eval) and yoloact model (segm eval)

Summary for 5000 imgs

Type	COCOeval	COCOeval_faster	Profit
bbox	18.477 sec.	7.345 sec.	~2.5x faster
segm	29.819 sec.	15.840 sec.	~1.7x faster

Summary for 500 imgs

Type	COCOeval	COCOeval_faster	Profit
bbox	3.57 sec.	2.03 sec.	~1.7x faster
segm	4.16 sec.	2.41 sec.	~1.7x faster

Feautures

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 and Wiki.

Usage

Code examples for using the library are available on the Wiki

Update history

Available via link history.md

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 for more information.

Citation

If you use this benchmark in your research, please cite this project.

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