Using Meters¶

VISSL supports PyTorch meters and implements some custom meters like Mean Average Precision meter. Meters in VISSL support multi target and multiple outputs. This is especially useful and relevant during the evaluation of self-supervised models where we want to measure 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 the meter requires. Users can also use multiple meters by setting METERS.names=["my_meter_name_one", "my_meter_name_two"].

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

Using Precision@k:

METERS:
  name: precision_at_k_list_meter
  precision_at_k_list_meter:
    num_meters: 1
    topk_values: [1]

Using Recall@k:

METERS:
  name: recall_at_k_list_meter
  recall_at_k_list_meter:
    num_meters: 1
    topk_values: [1]

Using Multiple Meters

METERS:
  names: [recall_at_k_list_meter, precision_at_k_list_meter]
  precision_at_k_list_meter:
    num_meters: 1
    topk_values: [1]
  recall_at_k_list_meter:
    num_meters: 1
    topk_values: [1]