diff --git a/val.py b/val.py
index 006ade37d03e..851d679d269b 100644
--- a/val.py
+++ b/val.py
@@ -182,7 +182,7 @@ def run(
 
     seen = 0
     confusion_matrix = ConfusionMatrix(nc=nc)
-    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
+    names = dict(enumerate(model.names if hasattr(model, 'names') else model.module.names))
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
     s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
     dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
@@ -250,7 +250,7 @@ def run(
 
             # Save/log
             if save_txt:
-                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
+                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
             if save_json:
                 save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
             callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
@@ -268,9 +268,7 @@ def run(
         tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
         ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
         mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
-        nt = np.bincount(stats[3].astype(int), minlength=nc)  # number of targets per class
-    else:
-        nt = torch.zeros(1)
+    nt = np.bincount(stats[3].astype(int), minlength=nc)  # number of targets per class
 
     # Print results
     pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format