diff --git a/modelscope/metrics/builder.py b/modelscope/metrics/builder.py
index 76278288..0357fa25 100644
--- a/modelscope/metrics/builder.py
+++ b/modelscope/metrics/builder.py
@@ -70,6 +70,7 @@ task_default_metrics = {
     [Metrics.image_quality_assessment_degradation_metric],
     Tasks.image_quality_assessment_mos:
     [Metrics.image_quality_assessment_mos_metric],
+    Tasks.bad_image_detecting: [Metrics.accuracy],
 }
 
 
diff --git a/modelscope/models/cv/bad_image_detecting/bad_image_detecting.py b/modelscope/models/cv/bad_image_detecting/bad_image_detecting.py
index f8cb866c..f173f479 100644
--- a/modelscope/models/cv/bad_image_detecting/bad_image_detecting.py
+++ b/modelscope/models/cv/bad_image_detecting/bad_image_detecting.py
@@ -10,6 +10,7 @@ from modelscope.metainfo import Models
 from modelscope.models.base import Tensor
 from modelscope.models.base.base_torch_model import TorchModel
 from modelscope.models.builder import MODELS
+from modelscope.outputs import OutputKeys
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
@@ -52,15 +53,14 @@ class BadImageDetecting(TorchModel):
 
         return {'output': ret}
 
-    def _evaluate_postprocess(self, input: Tensor,
-                              target: Tensor) -> Dict[str, list]:
+    def _evaluate_postprocess(self, input: Tensor) -> Dict[str, list]:
         torch.cuda.empty_cache()
         with torch.no_grad():
             preds = self.model(input)
             _, pred_ = torch.max(preds, dim=1)
         del input
         torch.cuda.empty_cache()
-        return {'pred': pred_, 'target': target}
+        return {OutputKeys.LABEL: pred_}
 
     def forward(self, inputs: Dict[str,
                                    Tensor]) -> Dict[str, Union[list, Tensor]]:
@@ -74,7 +74,8 @@ class BadImageDetecting(TorchModel):
         """
         if self.training:
             return self._train_forward(**inputs)
-        elif 'target' in inputs:
-            return self._evaluate_postprocess(**inputs)
+        elif OutputKeys.LABEL in inputs:
+            infeat = inputs['input']
+            return self._evaluate_postprocess(infeat)
         else:
             return self._inference_forward(**inputs)
diff --git a/modelscope/msdatasets/task_datasets/bad_image_detecting/bad_image_detecting_dataset.py b/modelscope/msdatasets/task_datasets/bad_image_detecting/bad_image_detecting_dataset.py
index 91ef5d13..f3cd9a2f 100644
--- a/modelscope/msdatasets/task_datasets/bad_image_detecting/bad_image_detecting_dataset.py
+++ b/modelscope/msdatasets/task_datasets/bad_image_detecting/bad_image_detecting_dataset.py
@@ -7,8 +7,9 @@ from modelscope.metainfo import Models
 from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
 from modelscope.msdatasets.task_datasets.torch_base_dataset import \
     TorchTaskDataset
+from modelscope.outputs import OutputKeys
 from modelscope.preprocessors import LoadImage
-from modelscope.preprocessors.cv.bad_image_preprocessor import \
+from modelscope.preprocessors.cv.bad_image_detecting_preprocessor import \
     BadImageDetectingPreprocessor
 from modelscope.utils.constant import Tasks
 
@@ -31,9 +32,11 @@ class BadImageDetectingDataset(TorchTaskDataset):
 
         # Load input video paths.
         item_dict = self.dataset[index]
-        iterm_label = item_dict['label']
-
-        img = LoadImage.convert_to_ndarray(input)
+        iterm_label = item_dict['category']
+        # print(input)
+        img = LoadImage.convert_to_ndarray(item_dict['image:FILE'])
         img = self.preprocessor(img)
-
-        return {'input': img['input'], 'target': iterm_label}
+        return {
+            'input': img['input'].squeeze(0),
+            OutputKeys.LABEL: iterm_label
+        }