mAP (mean Average Precision)

This code will evaluate the performance of your neural net for object recognition.

In practice, a higher mAP value indicates a better performance of your neural net, given your ground-truth and set of classes.

Citation

This repo is for my own use and has been worked on using https://github.com/Cartucho/mAP. My addition here is that it results in a result.csv that contains label level PR values.

Prerequisites

You need to install:

• 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
2. Copy the ground-truth files into the folder input/ground-truth/
3. Create the detection-results files
4. Copy the detection-results files into the folder input/detection-results/
5. Run the code: python main.py

Create the ground-truth files

• Create a separate ground-truth text file for each image.
• Use matching names for the files (e.g. image: "image_1.jpg", ground-truth: "image_1.txt").
• In these files, each line should be in the following format:

  <class_name> <left> <top> <right> <bottom> [<difficult>]
  

• The difficult parameter is optional, use it if you want the calculation to ignore a specific detection.
• E.g. "image_1.txt":

  tvmonitor 2 10 173 238
  book 439 157 556 241
  book 437 246 518 351 difficult
  pottedplant 272 190 316 259
  

Create the detection-results files

• Create a separate detection-results text file for each image.
• Use matching names for the files (e.g. image: "image_1.jpg", detection-results: "image_1.txt").
• In these files, each line should be in the following format:

  <class_name> <confidence> <left> <top> <right> <bottom>
  

• E.g. "image_1.txt":

  tvmonitor 0.471781 0 13 174 244
  cup 0.414941 274 226 301 265
  book 0.460851 429 219 528 247
  chair 0.292345 0 199 88 436
  book 0.269833 433 260 506 336