diff --git a/fastreid/evaluation/clas_evaluator.py b/fastreid/evaluation/clas_evaluator.py
index f40cc8e..763616a 100644
--- a/fastreid/evaluation/clas_evaluator.py
+++ b/fastreid/evaluation/clas_evaluator.py
@@ -46,11 +46,14 @@ class ClasEvaluator(DatasetEvaluator):
         self._predictions = []
 
     def process(self, inputs, outputs):
-        predictions = {
-            "logits": outputs.to(self._cpu_device, torch.float32),
-            "labels": inputs["targets"].to(self._cpu_device),
-        }
-        self._predictions.append(predictions)
+        pred_logits = outputs.to(self._cpu_device, torch.float32)
+        labels = inputs["targets"].to(self._cpu_device)
+
+        # measure accuracy
+        acc1, = accuracy(pred_logits, labels, topk=(1,))
+        num_correct_acc1 = acc1 * labels.size(0) / 100
+
+        self._predictions.append({"num_correct": num_correct_acc1, "num_samples": labels.size(0)})
 
     def evaluate(self):
         if comm.get_world_size() > 1:
@@ -63,21 +66,16 @@ class ClasEvaluator(DatasetEvaluator):
         else:
             predictions = self._predictions
 
-        pred_logits = []
-        labels = []
+        total_correct_num = 0
+        total_samples = 0
         for prediction in predictions:
-            pred_logits.append(prediction['logits'])
-            labels.append(prediction['labels'])
+            total_correct_num += prediction["num_correct"]
+            total_samples += prediction["num_samples"]
 
-        pred_logits = torch.cat(pred_logits, dim=0)
-        labels = torch.cat(labels, dim=0)
-
-        # measure accuracy and record loss
-        acc1, = accuracy(pred_logits, labels, topk=(1,))
+        acc1 = total_correct_num / total_samples * 100
 
         self._results = OrderedDict()
         self._results["Acc@1"] = acc1
-
         self._results["metric"] = acc1
 
         return copy.deepcopy(self._results)