This folder contains the script to calculate the mAP on COCO/Pascal VOC 2012 Standards. There are three modes of calculation supported as of now. i) Continues Point AP ii) 11-Point AP. iii) 101-Point AP. Continuous point AP is preferred as it is more standardized and accurate.

Usage of the same script is mentioned below

• Installing the requirements

python = 3.X

pip3 install -r requirements.txt

• Folder containing Ground Truths files(Text Files).

Each file will be containing annotations following the format: [Class_ID TLX TLy BRx BRy]. For example, there can be a file name frame_001.txt containing the following lines.

frame_001.txt
0 100. 200. 221.2 250.3
0 120.3 150.6 200.1 200.9

• Folder containing Detection files(Text Files).

NOTE: WHENEVER YOU GENERATE DETECTION FILES, DUMP THE RESULTS AT A VERY LOW CONFIDENCE THRESHOLD AROUND 0.001. THIS WILL ONLY GIVE YOU THE RIGHT mAP NUMBERS

Each file will be containing detection results following the format: **[Class_ID Conf TLx TLy BRx BRy] ** For example, there can be a filename frame_001.txt containing the following lines.

frame_001.txt
0 0.95 98 198.3 210.3 239.6

• File model.names that contains the names of the classes - index wise.

For example if the detector model is inferred on two classes say person and car, then model.names file will follow below given format.

model.names
person
car

NOTE: Please specify according to the class index that is being used in the detection files. Here, class person corresponds to index 0 as it is in the first line of model.names file and so on.

You can run the script by passing command line arguments with the following given switches. After running the command, log file evaluationLog.txt will be generated containing the AP results.

$python mAP.py [-h] [-d DET] [-g GT] [-i IOU] [-p POINTS] [-n NAMES] [-c CONFIDENCE]

Arguments:

  -d DET, --det DET     Full path to detection results folder
  -g GT, --gt GT        Full path to ground truth folder
  -i IOU, --iou IOU     Calculate AP at a particular IoU
  -p POINTS, --points POINTS
                        Interpolation value: 0: Continues / 11: PascalVOC2012 Challenge
  -n NAMES, --names NAMES
                        Full path of file containing names of classes index wise
  -o OUTPUT, --output OUTPUT
                        File to dump the output
  -c CONFIDENCE, --confidence CONFIDENCE
                        Confidence at which Precision/Recall is calculated
  -ig, --ignore         Flag to ignore the difficult anotations
  --coco                COCO Standard of calculation

Here in switch will contain the name of the model which you want to specify.

-c switch is used to calculate Precision and Recall at a mentioned confidence. Default is 0.5

-n switch is the path to the model.names file. Default is model.names.

-ig switch helps to remove the difficult annotations if ON. Remember, the format of the file should be classID, Diff(0/1), Tx, TLy, BRx, BRy

-o switch is the path to dump the results. After running the script, results for every class and for every IoU will get dumped in map_log.json(Default).

Demo Command: $ python3 mAP.py -g "$path/to/gt/files" -d "$path/to/detection/files" -i 0.5 -n coco.names -p 0 -c 0.5

You can expect the output something like given below on VOC standards

+---------------+-------+-------+------+-------+-----------+--------+-------+--------+
| Class/[email protected] |   AP  |   TP  |  FP  |   FN  | Precision | Recall |  GTs  |  Dets  |
+---------------+-------+-------+------+-------+-----------+--------+-------+--------+
|     Person    | 0.609 |  4271 | 338  |  5307 |   0.927   | 0.446  |  9578 | 409501 |
|    Bicycle    | 0.330 |  466  | 430  |  1437 |   0.520   | 0.245  |  1903 | 66510  |
|      Car      | 0.639 | 12138 | 842  | 24718 |   0.935   | 0.329  | 36856 | 408778 |
|   Motorcycle  | 0.221 |   35  | 292  |   53  |   0.107   | 0.398  |   88  | 23424  |
|      Bus      | 0.127 |  100  | 1002 |  119  |   0.091   | 0.457  |  219  | 21520  |
|     Truck     | 0.198 |  725  | 1875 |  5277 |   0.279   | 0.121  |  6002 | 60693  |
|    Backpack   | 0.183 |  136  | 136  |  845  |   0.500   | 0.139  |  981  | 15229  |
|    Handbag    | 0.210 |  125  | 140  |  1081 |   0.472   | 0.104  |  1206 | 26157  |
|    Suitcase   | 0.133 |   58  | 318  |  147  |   0.154   | 0.283  |  205  | 95856  |
+---------------+-------+-------+------+-------+-----------+--------+-------+--------+
mAP : 29.435 

I have referred to the official matlab code of mAP calculation and excellent written explanation and code by @rafaelpadilla to understand the mAP calculation and extend it to the COCO standards.

TODO: COCO small/medium/large AP calculation is in progress. Will be updated soon.