Using Meters

VISSL supports PyTorch meters and implements some custom meters that like Mean Average Precision meter. Meters in VISSL support single target multiple outputs. This is especially useful and relvant during evaluation of self-supervised models where we want to calculate feature quality of several layers of the model. See all the VISSL custom meters here.

To use a certain meter, users need to simply set METERS.name=<my_meter_name> and set the parameter values that meter requires.

Examples:

• Using Accuracy meter to compute Top-k accuracy for training and testing

METERS:
  name: accuracy_list_meter
  accuracy_list_meter:
    num_meters: 1          # number of outputs model has. also auto inferred
    topk_values: [1, 5]    # for each meter, what topk are computed.

• Using Mean AP meter:

METERS:
  name: mean_ap_list_meter
  mean_ap_list_meter:
    num_classes: 9605   # openimages v6 dataset classes
    num_meters: 1