diff --git a/README.md b/README.md
index 975b15a..61ac558 100644
--- a/README.md
+++ b/README.md
@@ -88,6 +88,45 @@ The decoded image data in towhee Image (a subset of numpy.ndarray).
 
  An image embedding generated by model, in shape of (feature_dim,).
 
+<br />
+
+***save_model(format='pytorch', path='default')***
+
+Save model to local with specified format.
+
+**Parameters:**
+
+***format***: *str*
+
+​	The format of saved model, defaults to 'pytorch'.
+
+***path***: *str*
+
+​	The path where model is saved to. By default, it will save model to the operator directory.
+
+<br />
+
+***supported_model_names(format=None)***
+
+Get a list of all supported model names or supported model names for specified model format.
+
+**Parameters:**
+
+***format***: *str*
+
+​	The model format such as 'pytorch', 'torchscript'.
+
+```python
+from towhee import ops
+
+
+op = ops.image_embedding.timm().get_op()
+full_list = op.supported_model_names()
+onnx_list = op.supported_model_names(format='onnx')
+print(f'Onnx-support/Total Models: {len(onnx_list)}/{len(full_list)}')
+```
+     2022-12-13 16:50:57,012 - 140704500614336 - timm_image.py-timm_image:76 - WARNING: The operator is initialized without specified model.
+     Onnx-support/Total Models: 736/770
 
 <br />
 
diff --git a/timm_image.py b/timm_image.py
index 6c75833..b56f3b2 100644
--- a/timm_image.py
+++ b/timm_image.py
@@ -54,10 +54,9 @@ class TimmImage(NNOperator):
     """
 
     def __init__(self,
-                 model_name: str = 'resnet50',
+                 model_name: str = None,
                  num_classes: int = 1000,
                  skip_preprocess: bool = False,
-                 pretrained: bool = True,
                  device: str = None
                  ) -> None:
         super().__init__()
@@ -65,13 +64,17 @@ class TimmImage(NNOperator):
             device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.device = device
         self.model_name = model_name
-        self.model = create_model(self.model_name, pretrained=pretrained, num_classes=num_classes)
-        self.model.eval()
-        self.model.to(self.device)
-
-        self.config = resolve_data_config({}, model=self.model)
-        self.tfms = create_transform(**self.config)
-        self.skip_tfms = skip_preprocess
+        if self.model_name:
+            self.model = create_model(self.model_name, pretrained=pretrained, num_classes=num_classes)
+            self.model.eval()
+            self.model.to(self.device)
+
+            self.config = resolve_data_config({}, model=self.model)
+            self.tfms = create_transform(**self.config)
+            self.skip_tfms = skip_preprocess
+        else:
+            log.warning('The operator is initialized without specified model.')
+            pass
 
     def __call__(self, data: Union[List[towhee._types.Image], towhee._types.Image]):
         if not isinstance(data, list):
@@ -148,16 +151,73 @@ class TimmImage(NNOperator):
 
     @staticmethod
     def supported_model_names(format: str = None):
+        assert timm.__version__ == '0.6.12', 'The model lists are tested with timm==0.6.12.'
         full_list = timm.list_models(pretrained=True)
         full_list.sort()
         if format is None:
             model_list = full_list
         elif format == 'pytorch':
-            to_remove = []
+            to_remove = [
+                'coat_mini',
+                'coat_tiny',
+                'crossvit_9_240',
+                'crossvit_9_dagger_240',
+                'crossvit_15_240',
+                'crossvit_15_dagger_240',
+                'crossvit_15_dagger_408',
+                'crossvit_18_240',
+                'crossvit_18_dagger_240',
+                'crossvit_18_dagger_408',
+                'crossvit_base_240',
+                'crossvit_small_240',
+                'crossvit_tiny_240',
+                'tresnet_l',
+                'tresnet_l_448',
+                'tresnet_m',
+                'tresnet_m_448',
+                'tresnet_m_miil_in21k',
+                'tresnet_v2_l',
+                'tresnet_xl',
+                'tresnet_xl_448']
             assert set(to_remove).issubset(set(full_list))
             model_list = list(set(full_list) - set(to_remove))
         elif format == 'onnx':
-            to_remove = []
+            to_remove = [
+                'bat_resnext26ts',
+                'convmixer_768_32',
+                'convmixer_1024_20_ks9_p14',
+                'convmixer_1536_20',
+                'eca_halonext26ts',
+                'efficientformer_l1',
+                'efficientformer_l3',
+                'efficientformer_l7',
+                'halo2botnet50ts_256',
+                'halonet26t',
+                'halonet50ts',
+                'haloregnetz_b',
+                'lamhalobotnet50ts_256',
+                'levit_128',
+                'levit_128s',
+                'levit_192',
+                'levit_256',
+                'levit_384',
+                'pvt_v2_b2_li',
+                'sehalonet33ts',
+                'tf_efficientnet_cc_b0_4e',
+                'tf_efficientnet_cc_b0_8e',
+                'tf_efficientnet_cc_b1_8e',
+                'volo_d1_224',
+                'volo_d1_384',
+                'volo_d2_224',
+                'volo_d2_384',
+                'volo_d3_224',
+                'volo_d3_448',
+                'volo_d4_224',
+                'volo_d4_448',
+                'volo_d5_224',
+                'volo_d5_448',
+                'volo_d5_512'
+            ]
             assert set(to_remove).issubset(set(full_list))
             model_list = list(set(full_list) - set(to_remove))
         # todo: elif format == 'torchscript':