fiftyone.utils.eval.base.BaseClassificationResults

Undocumented

Undocumented

Undocumented

Clears the subset set by use_subset, if any.

Subsequent operations will be performed on the full results.

Generates a confusion matrix for the results via sklearn:sklearn.metrics.confusion_matrix.

The rows of the confusion matrix represent ground truth and the columns represent predictions.

Computes classification metrics for the results, including accuracy, precision, recall, and F-beta score.

See sklearn:sklearn.metrics.precision_recall_fscore_support for details.

Also includes any custom metrics from custom_metrics.

Plots a confusion matrix for the evaluation results.

If you are working in a notebook environment with the default plotly backend, this method returns an interactive fiftyone.core.plots.plotly.InteractiveHeatmap that you can attach to an App session via its fiftyone.core.session.Session.plots attribute, which will automatically sync the session's view with the currently selected cells in the confusion matrix.

Prints the metrics computed via metrics.

Prints a classification report for the results via sklearn:sklearn.metrics.classification_report.

Generates a classification report for the results via sklearn:sklearn.metrics.classification_report.

Restricts the evaluation results to the specified subset.

Subsequent calls to supported methods on this instance will only contain results from the specified subset rather than the full results.

Use clear_subset to reset to the full results. Or, equivalently, use the context manager interface as demonstrated below to automatically reset the results when the context exits.

Example usage:

import fiftyone as fo
import fiftyone.zoo as foz
import fiftyone.utils.random as four
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")
four.random_split(dataset, {"sunny": 0.7, "cloudy": 0.2, "rainy": 0.1})

results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
)

counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:5]

# Full results
results.print_report(classes=classes)

# Sunny samples
subset_def = dict(type="field", field="tags", value="sunny")
with results.use_subset(subset_def):
    results.print_report(classes=classes)

# Small objects
bbox_area = F("bounding_box")[2] * F("bounding_box")[3]
small_objects = bbox_area <= 0.05
subset_def = dict(type="attribute", expr=small_objects)
with results.use_subset(subset_def):
    results.print_report(classes=classes)