API Reference: Evaluation¶
- class masterful.evaluation.detection.coco.CocoEvaluationMetrics(categories, include_metrics_per_category=False, all_metrics_per_category=False, skip_predictions_for_unlabeled_class=False, super_categories=None)¶
Class for evaluating the MSCOCO evaluation metrics against the outputs from a Keras based object detection model.
This relies on the pycocotools package from the MSCOCO official release to perform the evaluation.
- __init__(categories, include_metrics_per_category=False, all_metrics_per_category=False, skip_predictions_for_unlabeled_class=False, super_categories=None)¶
Initializes a new instance of CocoEvaluationMetrics.
categories – A list of dicts, each of which has the following keys - ‘id’: (required) an integer id uniquely identifying this category. ‘name’: (required) string representing category name e.g., ‘cat’, ‘dog’.
include_metrics_per_category – If True, include metrics for each category.
all_metrics_per_category – Whether to include all the summary metrics for each category in per_category_ap. Be careful with setting it to true if you have more than handful of categories, because it will pollute your mldash.
skip_predictions_for_unlabeled_class – Skip predictions that do not match with the labeled classes for the image.
super_categories – None or a python dict mapping super-category names (strings) to lists of categories (corresponding to category names in the label_map). Metrics are aggregated along these super-categories and added to the per_category_ap and are associated with the name PerformanceBySuperCategory/<super-category-name>.
Clears the state to prepare for a fresh evaluation.
- evaluate_model(model, predictions_to_labels, test_dataset, num_classes, max_examples=9223372036854775807)¶
Evaluates a Keras based detection model and returns the COCO statistics.
model (keras.engine.training.Model) – The model to use for predictions.
predictions_to_labels (Callable) – A Callbable function which converts model predictions into labels that can be evaluated using the COCO metrics.
test_dataset (tensorflow.python.data.ops.dataset_ops.DatasetV2) – The dataset to measure over.
num_classes (int) – The number of classes in the dataset and model predictions.
max_examples (int) – Maximum number of examples to evaluate. Defaults to all examples.
A dictionary of COCO evaluation metrics.
- Return type
Saves the detections into json_output_path in the format used by MS COCO.
json_output_path – String containing the output file’s path. It can be also None. In that case nothing will be written to the output file.