mn

app.py

@@ -115,8 +115,8 @@ def count_big_cats(img, score_threshold, iou_threshold, model_id):
     detector = get_detector(model_id)
     raw = detector(img, candidate_labels=CANDIDATE_LABELS)
 
-    # Canonicalize labels BEFORE NMS so synonyms don't double-count
-    preds = class_aware_nms(raw, iou_thresh=iou_threshold)
+    # 1) Filter by score and canonicalize labels to {"tiger","lion"}
+    preds = []
     for p in raw:
         if p["score"] < score_threshold:
             continue
@@ -124,11 +124,10 @@ def count_big_cats(img, score_threshold, iou_threshold, model_id):
         if canon is None:
             continue
         q = dict(p)
-        q["label"] = canon           # overwrite with canonical ('tiger'/'lion')
+        q["label"] = canon  # overwrite with canonical
         preds.append(q)
 
-    # NMS per canonical class
-    iou_threshold = iou_threshold
+    # 2) NMS per canonical class (avoids double-counting synonyms like "toy tiger")
     preds = class_aware_nms(preds, iou_thresh=iou_threshold)
 
     tiger_count = sum(1 for p in preds if p["label"] == "tiger")
@@ -138,6 +137,7 @@ def count_big_cats(img, score_threshold, iou_threshold, model_id):
     img_annotated = annotate(img.copy(), preds)
     return tiger_count, lion_count, total_count, img_annotated
 
+
 # ---------- Demo ----------
 TEST_IMAGES = {
     "Tigers": "examples/tiger1.png",