Datasets:
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7825400 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | """Image-level multi-label classification metrics."""
from collections import defaultdict
from metrics._f_beta import ViolationCounts, aggregate_summary, summarise_counts
from metrics._labels import to_use_case_id
from schemas.ground_truth import GoldRecord
from schemas.inference import InferenceResponse
def compute_classification_metrics(
predictions: list[InferenceResponse], ground_truths: list[GoldRecord]
) -> dict:
"""Compare class sets; repeated detections of one class count once."""
gt_by_id = {record.image_id: record for record in ground_truths}
counts: dict[str, ViolationCounts] = defaultdict(ViolationCounts)
negatives = true_negatives = false_positive_images = evaluated = 0
for prediction in predictions:
record = gt_by_id.get(prediction.image_id)
if record is None or record.gold.reported:
continue
evaluated += 1
gt_labels = {item.use_case_id for item in record.gold.violations}
pred_labels = {to_use_case_id(item.category_id) for item in prediction.detections}
for label in gt_labels | pred_labels:
target = counts[label]
if label in gt_labels and label in pred_labels:
target.tp += 1
elif label in pred_labels:
target.fp += 1
else:
target.fn += 1
if record.gold.no_violation_detected:
negatives += 1
if pred_labels:
false_positive_images += 1
else:
true_negatives += 1
return {
"per_violation": summarise_counts(counts),
"summary": aggregate_summary(counts),
"n_images": evaluated,
"negative_images": negatives,
"true_negative_images": true_negatives,
"false_positive_images": false_positive_images,
"negative_false_positive_rate": round(false_positive_images / negatives, 4) if negatives else None,
}
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