diff --git a/mmpretrain/datasets/scienceqa.py b/mmpretrain/datasets/scienceqa.py
index f6ae7f83..96826448 100644
--- a/mmpretrain/datasets/scienceqa.py
+++ b/mmpretrain/datasets/scienceqa.py
@@ -69,7 +69,7 @@ class ScienceQA(BaseDataset):
                 ann['question'],
                 'choices':
                 ann['choices'],
-                'answer':
+                'gt_answer':
                 ann['answer'],
                 'hint':
                 ann['hint'],
@@ -98,6 +98,8 @@ class ScienceQA(BaseDataset):
                 if ann['image'] is not None else None,
                 'caption':
                 ann['caption'],
+                'has_image':
+                True if ann['image'] is not None else False,
             }
             data_list.append(data_info)
 
diff --git a/mmpretrain/evaluation/metrics/scienceqa.py b/mmpretrain/evaluation/metrics/scienceqa.py
index f81d4ec1..fab5c97c 100644
--- a/mmpretrain/evaluation/metrics/scienceqa.py
+++ b/mmpretrain/evaluation/metrics/scienceqa.py
@@ -56,7 +56,7 @@ class ScienceQAMetric(BaseMetric):
         """Process one batch of data samples.
 
         data_samples should contain the following keys:
-        1. prediction (str): The prediction from the model,
+        1. pred_answer (str): The prediction from the model,
             from ['A', 'B', 'C', 'D', 'E']
         2. choices (List(str)): The choices for the question,
             from ['A', 'B', 'C', 'D', 'E']
@@ -66,7 +66,7 @@ class ScienceQAMetric(BaseMetric):
         5. answer (str): The answer for the question, from
             ['A', 'B', 'C', 'D', 'E']
         6. hint (str): The hint for the question
-        7. image (torch.Tensor): The image for the question
+        7. has_image (bool): Whether or not the question has image
 
 
         The processed results should be stored in ``self.results``, which will
@@ -80,7 +80,7 @@ class ScienceQAMetric(BaseMetric):
             result = dict()
             choices = data_sample.get('choices')
             result['prediction'] = get_pred_idx(
-                data_sample.get('prediction'), choices, self.options)
+                data_sample.get('pred_answer'), choices, self.options)
             result['grade'] = data_sample.get('grade')
             result['subject'] = data_sample.get('subject')
             result['answer'] = self.options[data_sample.get('answer')]
@@ -89,7 +89,7 @@ class ScienceQAMetric(BaseMetric):
             result[
                 'no_context'] = True if image is None and hint is None else False  # noqa
             result['has_text'] = True if hint is not None else False
-            result['has_image'] = True if image is not None else False
+            result['has_image'] = data_sample.get('has_image', False)
 
             # Save the result to `self.results`.
             self.results.append(result)