diff --git a/docs/en/user_guides/inference.md b/docs/en/user_guides/inference.md
index a1c970c1e..af66846a5 100644
--- a/docs/en/user_guides/inference.md
+++ b/docs/en/user_guides/inference.md
@@ -38,10 +38,10 @@ model = init_model(config_path, checkpoint_path, device="cpu")  # device can be
 result = inference_model(model, img_path)
 ```
 
-`result` is a dictionary containing `pred_label`, `pred_score` and `pred_score`, the result is as follows:
+`result` is a dictionary containing `pred_label`, `pred_score`, `pred_scores` and `pred_class`, the result is as follows:
 
 ```text
-{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake"}
+{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}
 ```
 
 An image demo can be found in [demo/image_demo.py](https://github.com/open-mmlab/mmclassification/blob/1.x/demo/image_demo.py).
diff --git a/docs/zh_CN/user_guides/inference.md b/docs/zh_CN/user_guides/inference.md
index b1f0afca1..35c745618 100644
--- a/docs/zh_CN/user_guides/inference.md
+++ b/docs/zh_CN/user_guides/inference.md
@@ -39,10 +39,10 @@ result = inference_model(model, img_path)
 print(result)
 ```
 
-`result` 为一个包含了 `pred_label`, `pred_score` 和 `pred_score`的字典，结果如下:
+`result` 为一个包含了 `pred_label`, `pred_score`, `pred_scores` 和 `pred_class`的字典，结果如下:
 
 ```text
-{"pred_label":65,"pred_score":0.6649366617202759,"pred_score":"sea snake"}
+{"pred_label":65,"pred_score":0.6649366617202759,"pred_class":"sea snake", "pred_scores": [..., 0.6649366617202759, ...]}
 ```
 
 演示可以在 [demo/image_demo.py](https://github.com/open-mmlab/mmclassification/blob/1.x/demo/image_demo.py) 中找到。
diff --git a/mmcls/apis/inference.py b/mmcls/apis/inference.py
index c2a294294..80ddf6f37 100644
--- a/mmcls/apis/inference.py
+++ b/mmcls/apis/inference.py
@@ -80,9 +80,14 @@ def inference_model(model, img):
     # forward the model
     with torch.no_grad():
         prediction = model.val_step(data)[0].pred_label
+        pred_scores = prediction.score.tolist()
         pred_score = torch.max(prediction.score).item()
         pred_label = torch.argmax(prediction.score).item()
-        result = {'pred_label': pred_label, 'pred_score': float(pred_score)}
+        result = {
+            'pred_label': pred_label,
+            'pred_score': float(pred_score),
+            'pred_scores': pred_scores
+        }
     if hasattr(model, 'CLASSES'):
         result['pred_class'] = model.CLASSES[result['pred_label']]
     return result