Underlying libraries

The current metrics module relies on:

• torchmetrics for classification and detection metric implementations

• faster-coco-eval as the default backend for detection mean average precision

To tweak specific options via the RAITAP config, you might need to refer to the underlying library’s documentation.

Classification metrics

ClassificationMetrics is ideal for classification models. It wraps the following TorchMetrics classes:

• Accuracy

• Precision

• Recall

• F1Score

It supports the task types exposed by TorchMetrics that RAITAP currently uses:

• binary

• multiclass

• multilabel

RAITAP adds a thin layer of validation and conventions around these metrics. In particular:

• multiclass requires num_classes

• multilabel uses num_labels internally and also accepts num_classes as an alias. It defaults to a threshold of 0.5 if none is provided

• average="none" stores per-class or per-label values in artifacts.json instead of flattening them into scalar metrics

For typical classification runs, the scalar results written to metrics/metrics.json are accuracy, precision, recall, and f1.

Detection metrics

DetectionMetrics is ideal for object detection models. It wraps TorchMetrics’ MeanAveragePrecision.

Detection inputs follow the structure expected by TorchMetrics: a list of prediction dictionaries and a list of target dictionaries, usually one entry per image. Predictions include boxes, scores, and labels, while targets include boxes and labels.

Scalar detection results such as map are written to metrics.json. Larger structured outputs, such as class-wise or extended summaries, are written to artifacts.json.