diff --git a/ultralytics/engine/validator.py b/ultralytics/engine/validator.py
index 655f2455ca..160a549d64 100644
--- a/ultralytics/engine/validator.py
+++ b/ultralytics/engine/validator.py
@@ -110,7 +110,8 @@ class BaseValidator:
         if self.training:
             self.device = trainer.device
             self.data = trainer.data
-            self.args.half = self.device.type != "cpu"  # force FP16 val during training
+            # force FP16 val during training
+            self.args.half = self.device.type != "cpu" and self.args.amp
             model = trainer.ema.ema or trainer.model
             model = model.half() if self.args.half else model.float()
             # self.model = model
diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py
index 6b308bc146..70d3d088b4 100644
--- a/ultralytics/utils/checks.py
+++ b/ultralytics/utils/checks.py
@@ -656,9 +656,10 @@ def check_amp(model):
 
     def amp_allclose(m, im):
         """All close FP32 vs AMP results."""
-        a = m(im, device=device, verbose=False)[0].boxes.data  # FP32 inference
+        batch = [im] * 8
+        a = m(batch, imgsz=128, device=device, verbose=False)[0].boxes.data  # FP32 inference
         with autocast(enabled=True):
-            b = m(im, device=device, verbose=False)[0].boxes.data  # AMP inference
+            b = m(batch, imgsz=128, device=device, verbose=False)[0].boxes.data  # AMP inference
         del m
         return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.5)  # close to 0.5 absolute tolerance