diff --git a/test.py b/test.py
index bdcf8cf5..dc6c9f50 100644
--- a/test.py
+++ b/test.py
@@ -126,7 +126,7 @@ def test(cfg,
             # Assign all predictions as incorrect
             correct = torch.zeros(len(pred), niou)
             if nl:
-                detected = []
+                detected = []  # target indices
                 tcls_tensor = labels[:, 0]
 
                 # target boxes
@@ -134,26 +134,24 @@ def test(cfg,
                 tbox[:, [0, 2]] *= width
                 tbox[:, [1, 3]] *= height
 
-                # Search for correct predictions
-                for i, (*pbox, _, pcls) in enumerate(pred):
+                # Per target class
+                for cls in torch.unique(tcls_tensor):
+                    ti = (cls == tcls_tensor).nonzero().view(-1)  # prediction indices
+                    pi = (cls == pred[:, 5]).nonzero().view(-1)  # target indices
 
-                    # Break if all targets already located in image
-                    if len(detected) == nl:
-                        break
+                    # Search for detections
+                    if len(pi):
+                        # Prediction to target ious
+                        ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1)  # best ious, indices
 
-                    # Continue if predicted class not among image classes
-                    if pcls.item() not in tcls:
-                        continue
-
-                    # Best iou, index between pred and targets
-                    m = (pcls == tcls_tensor).nonzero().view(-1)
-                    iou, j = bbox_iou(pbox, tbox[m]).max(0)
-                    m = m[j]
-
-                    # Per iou_thres 'correct' vector
-                    if iou > iou_thres[0] and m not in detected:
-                        detected.append(m)
-                        correct[i] = iou > iou_thres
+                        # Append detections
+                        for j in (ious > iou_thres[0]).nonzero():
+                            d = ti[i[j]]  # detected target
+                            if d not in detected:
+                                detected.append(d)
+                                correct[pi[j]] = (ious[j] > iou_thres).float()  # iou_thres is 1xn
+                                if len(detected) == nl:  # all targets already located in image
+                                    break
 
             # Append statistics (correct, conf, pcls, tcls)
             stats.append((correct, pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))