vis4d.eval.coco

Detection evaluators.

class COCODetectEvaluator(data_root, split='val2017', per_class_eval=False)

COCO detection evaluation class.

__init__(data_root, split='val2017', per_class_eval=False)

Creates an instance of the class.

Parameters:

• data_root (str) – Root directory of data.

• split (str, optional) – COCO data split. Defaults to “val2017”.

• per_class_eval (bool, optional) – Per-class evaluation. Defaults to False.

property metrics: list[str]

Supported metrics.

Returns:

Metrics to evaluate.

Return type:

list[str]

gather(gather_func)

Accumulate predictions across processes.

Return type:

None

reset()

Reset the saved predictions to start new round of evaluation.

Return type:

None

process_batch(coco_image_id, pred_boxes, pred_scores, pred_classes, pred_masks=None)

Process sample and convert detections to coco format.

coco_image_id (list[int]): COCO image ID. pred_boxes (list[ArrayLike]): Predicted bounding boxes. pred_scores (list[ArrayLike]): Predicted scores for each box. pred_classes (list[ArrayLike]): Predicted classes for each box. pred_masks (None | list[ArrayLike], optional): Predicted masks.

Return type:

None

evaluate(metric)

Evaluate COCO predictions.

Parameters:

metric (str) – Metric to evaluate. Should be “COCO_AP”.

Raises:

• NotImplementedError – Raised if metric is not “COCO_AP”.

• RuntimeError – Raised if no predictions are available.

Returns:

Dictionary of scores to log and a pretty

printed string.

Return type:

tuple[MetricLogs, str]

__repr__()

Returns the string representation of the object.

Return type:

str

Modules

detect

COCO evaluator.