test.py native --single-cls (ultralytics#2928)

test.py

@@ -119,7 +119,7 @@ def test(data,
             targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
             lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
             t = time_synchronized()
-            out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
+            out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
             t1 += time_synchronized() - t
 
         # Statistics per image
@@ -136,6 +136,8 @@ def test(data,
                 continue
 
             # Predictions
+            if single_cls:
+                pred[:, 5] = 0
             predn = pred.clone()
             scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])  # native-space pred