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mAP_demo.md

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Run the mAP demo

To compute mAP, precision, recall and f1score to evaluate 2D object detectors, run the map_demo.

A validation set is needed. To download COCO_val2017 (80 classes) run (form the root folder):

bash scripts/download_validation.sh COCO

To download Berkeley_val (10 classes) run (form the root folder):

bash scripts/download_validation.sh BDD

To compute the map, the following parameters are needed:

./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>

where

  • <network rt>: rt file of a chosen network on which compute the mAP.
  • <network type [y|c|m]>: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
  • <labels file path>: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg.
  • <config file path>: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml

Example:

cd build
./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml

This demo also creates a json file named net_name_COCO_res.json containing all the detections computed. The detections are in COCO format, the correct format to submit the results to CodaLab COCO detection challenge.