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Last Commit
Aug. 15, 2018
Mar. 8, 2018

mAP (mean Average Precision)

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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.

Table of contents


The performance of your neural net will be judged using the mAP criterium defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code 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 predicted objects 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).

Using this criterium, we calculate the precision/recall curve. E.g:

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:


You need to install:


  • plot the results by installing Matplotlib - Linux, macOS and Windows:
    1. python -mpip install -U pip
    2. python -mpip install -U matplotlib
  • show animation by installing OpenCV:
    1. python -mpip install -U pip
    2. python -mpip install -U opencv-python


To start using the mAP you need to clone the repo:

git clone

Running the code

Step by step:

  1. Create the ground-truth files
  2. Move the ground-truth files into the folder ground-truth/
  3. Create the predicted objects files
  4. Move the predictions files into the folder predicted/
  5. Run the code: python

Optional (if you want to see the animation):

  1. Insert the images into the folder images/

PASCAL VOC, Darkflow and YOLO users

In the extra folder you can find additional scripts to convert PASCAL VOC, darkflow and YOLO files into the required format.

Create the ground-truth files

  • Create a separate ground-truth text file for each image.
  • Use matching names (e.g. image: "image_1.jpg", ground-truth: "image_1.txt"; "image_2.jpg", "image_2.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 to ignore a specific prediction.
  • 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 predicted objects files

  • Create a separate predicted objects text file for each image.
  • Use matching names (e.g. image: "image_1.jpg", predicted: "image_1.txt"; "image_2.jpg", "image_2.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


  • João Cartucho - Please give me your feedback: [email protected]

    Feel free to contribute

    GitHub contributors

Latest Releases
First Release
 Apr. 9 2018