diff --git a/classify/train.py b/classify/train.py
index 9fb7c52b545a..5881e16e47db 100644
--- a/classify/train.py
+++ b/classify/train.py
@@ -122,16 +122,16 @@ def train(opt, device):
     for p in model.parameters():
         p.requires_grad = True  # for training
     model = model.to(device)
-    names = trainloader.dataset.classes  # class names
-    model.names = names  # attach class names
 
     # Info
     if RANK in {-1, 0}:
+        model.names = trainloader.dataset.classes  # attach class names
+        model.transforms = testloader.dataset.torch_transforms  # attach inference transforms
         model_info(model)
         if opt.verbose:
             LOGGER.info(model)
         images, labels = next(iter(trainloader))
-        file = imshow_cls(images[:25], labels[:25], names=names, f=save_dir / 'train_images.jpg')
+        file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg')
         logger.log_images(file, name='Train Examples')
         logger.log_graph(model, imgsz)  # log model
 
@@ -254,8 +254,8 @@ def train(opt, device):
 
         # Plot examples
         images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
-        pred = torch.max(ema.ema((images.half() if cuda else images.float()).to(device)), 1)[1]
-        file = imshow_cls(images, labels, pred, names, verbose=False, f=save_dir / 'test_images.jpg')
+        pred = torch.max(ema.ema(images.to(device)), 1)[1]
+        file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
 
         # Log results
         meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
diff --git a/classify/val.py b/classify/val.py
index c91e2cf82c81..2353737957d3 100644
--- a/classify/val.py
+++ b/classify/val.py
@@ -39,7 +39,7 @@ def run(
     project=ROOT / 'runs/val-cls',  # save to project/name
     name='exp',  # save to project/name
     exist_ok=False,  # existing project/name ok, do not increment
-    half=True,  # use FP16 half-precision inference
+    half=False,  # use FP16 half-precision inference
     dnn=False,  # use OpenCV DNN for ONNX inference
     model=None,
     dataloader=None,
@@ -124,7 +124,6 @@ def run(
         LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
 
-    model.float()  # for training
     return top1, top5, loss