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mean Average Precision - This code evaluates the performance of your neural net for object recognition.
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# mAP (mean Average Precision)
[](https://github.com/Cartucho/mAP)
This code will evaluate the performance of your neural net for object recognition.
<p align="center">
<img src="https://user-images.githubusercontent.com/15831541/37559643-6738bcc8-2a21-11e8-8a07-ed836f19c5d9.gif" width="450" height="300" />
</p>
In practice, a **higher mAP** value indicates a **better performance** of your neural net, given your ground-truth and set of classes.
## Citation
This project was developed for the following paper, please consider citing it:
```bibtex
@INPROCEEDINGS{8594067,
author={J. {Cartucho} and R. {Ventura} and M. {Veloso}},
booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots},
year={2018},
pages={2336-2341},
}
```
## Table of contents
- [Explanation](#explanation)
- [Prerequisites](#prerequisites)
- [Quick start](#quick-start)
- [Running the code](#running-the-code)
- [Authors](#authors)
## Explanation
The performance of your neural net will be judged using the mAP criterium defined in the [PASCAL VOC 2012 competition](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/). We simply adapted the [official Matlab code](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit) into Python (in our tests they both give the same results).
First (**1.**), we calculate the Average Precision (AP), for each of the classes present in the ground-truth. Finally (**2.**), we calculate the mAP (mean Average Precision) value.
#### 1. Calculate AP
For each class:
First, your neural net **detection-results** are sorted by decreasing confidence and are assigned to **ground-truth objects**. We have "a match" when they share the **same label and an IoU >= 0.5** (Intersection over Union greater than 50%). This "match" is considered a true positive if that ground-truth object has not been already used (to avoid multiple detections of the same object).
<img src="https://user-images.githubusercontent.com/15831541/37725175-45b9e1a6-2d2a-11e8-8c15-2fb4d716ca9a.png" width="35%" height="35%" />
Using this criterium, we calculate the precision/recall curve. E.g:
<img src="https://user-images.githubusercontent.com/15831541/43008995-64dd53ce-8c34-11e8-8a2c-4567b1311910.png" width="45%" height="45%" />
Then we compute a version of the measured precision/recall curve with **precision monotonically decreasing** (shown in light red), by setting the precision for recall `r` to the maximum precision obtained for any recall `r' > r`.
Finally, we compute the AP as the **area under this curve** (shown in light blue) by numerical integration.
No approximation is involved since the curve is piecewise constant.
#### 2. Calculate mAP
We calculate the mean of all the AP's, resulting in an mAP value from 0 to 100%. E.g:
<img src="https://user-images.githubusercontent.com/15831541/38933241-5f9556ae-4310-11e8-9d47-cb205f9b103b.png"/>
<img src="https://user-images.githubusercontent.com/15831541/38933180-366b6fca-4310-11e8-99b9-17ad4b159b86.png" />
## Prerequisites
You need to install:
- [Python](https://www.python.org/downloads/)
Optional:
- **plot** the results by [installing Matplotlib](https://matplotlib.org/users/installing.html) - Linux, macOS and Windows:
1. `python -mpip install -U pip`
2. `python -mpip install -U matplotlib`
- show **animation** by installing [OpenCV](https://www.opencv.org/):
1. `python -mpip install -U pip`
2. `python -mpip install -U opencv-python`
## Quick-start
To start using the mAP you need to clone the repo:
```
git clone https://github.com/Cartucho/mAP
```
## Running the code
Step by step:
1. [Create the ground-truth files](#create-the-ground-truth-files)
2. Copy the ground-truth files into the folder **input/ground-truth/**
3. [Create the detection-results files](Excerpt of 5,913 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f414a69ada703a38, topic:computer-vision, topic:object-detection, topic:yolo
