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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.
| Date | Stars |
|---|---|
| 2026-07-24 | 1166 |
| 2026-07-25 | 1167 |
| 2026-07-28 | 1167 |
| 2026-07-30 | 1167 |
| 2026-08-06 | 1167 |
Today
— stars today
This week
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This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="left">
<a>[](https://travis-ci.com/rafaelpadilla/review_object_detection_metrics)</a>
<a href="https://github.com/rafaelpadilla/review_object_detection_metrics/raw/main/published_paper.pdf">
<img src="https://img.shields.io/badge/paper-published-blue"/></a>
<a><img src="https://img.shields.io/badge/version-0.1-orange"/></a>
<a href="https://doi.org/10.3390/electronics10030279">
<img src="https://img.shields.io/badge/DOI-10.3390%2Felectronics10030279-gray"/></a>
</p>
## Citation
This work was published in the [Journal Electronics - Special Issue Deep Learning Based Object Detection](https://www.mdpi.com/2079-9292/10/3/279).
If you use this code for your research, please consider citing:
```
@Article{electronics10030279,
AUTHOR = {Padilla, Rafael and Passos, Wesley L. and Dias, Thadeu L. B. and Netto, Sergio L. and da Silva, Eduardo A. B.},
TITLE = {A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit},
JOURNAL = {Electronics},
VOLUME = {10},
YEAR = {2021},
NUMBER = {3},
ARTICLE-NUMBER = {279},
URL = {https://www.mdpi.com/2079-9292/10/3/279},
ISSN = {2079-9292},
DOI = {10.3390/electronics10030279}
}
```
Download the paper [here](https://www.mdpi.com/2079-9292/10/3/279/pdf) or [here](https://github.com/rafaelpadilla/review_object_detection_metrics/blob/main/published_paper.pdf).
Open-Source Visual Interface for Object Detection Metrics
================================================
Our [previously available tool](https://github.com/rafaelpadilla/Object-Detection-Metrics) for object detection assessment has received many positive feedbacks, which motivated us to upgrade it with other metrics and support more bounding box formats. As some external tools, competitions and works are already using the older version, we decided not to modify it but release a newer and more complete project.
The motivation of this project is the lack of consensus used by different works and implementations concerning the evaluation metrics of the object detection problem. Although on-line competitions use their own metrics to evaluate the task of object detection, just some of them offer reference code snippets to calculate the assertiveness of the detected objects.
Researchers, who want to evaluate their work using different datasets than those offered by the competitions, need to implement their own version of the metrics or spend a considerable amount of time converting their bounding boxes to formats that are supported by evaluation tools. Sometimes a wrong or different implementation can create different and biased results. Even though many tools have been developed to convert the annotated boxes from one type to another, the quality assessment of the final detections still lacks a tool compatible with different bounding box formats and multiple performance metrics.
Ideally, in order to have trustworthy benchmarking among different approaches, it is necessary to have an implementation that can be used by everyone regardless the dataset used. This work attempts to cover this gap, providing an open-source tool flexible to support many bounding box formats and evaluate detections with different metrics (AP@[.5:.05:.95], AP@50, mAP, AR<sub>1</sub>, AR<sub>10</sub>, AR<sub>100</sub>, etc). We also provide a detailed explanation pointing out their divergences, showing how different implementations may result into different results.
## Table of contents
- [Open-Source Toolbox for Object Detection Metrics](#open-source-toolbox-for-object-detection-metrics)
- [Table of contents](#table-of-contents)
- [Supported bounding box formats](#supported-bounding-box-formats)
- [A practical example](#a-practical-example)
- [Metrics](#metrics)
- [AP with IOU Threshold *t=0.5*](#ap-with-iou-threshold-t05)
- [mAP with IOU Threshold *t=0.5*](#map-with-iou-threshold-t05)
- [[email protected]Excerpt of 24,612 characters
Read on GitHub
Rafael Padilla
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5495fa4ce1b4f8b4, topic:object-detection, name:object detection, desc:object detection