diff --git a/src/transformers/onnx/config.py b/src/transformers/onnx/config.py
index 1c8d10939a1..bbf06b07929 100644
--- a/src/transformers/onnx/config.py
+++ b/src/transformers/onnx/config.py
@@ -28,6 +28,7 @@ from .utils import ParameterFormat, compute_effective_axis_dimension, compute_se
 if TYPE_CHECKING:
     from ..configuration_utils import PretrainedConfig
     from ..feature_extraction_utils import FeatureExtractionMixin
+    from ..image_processing_utils import ImageProcessingMixin
     from ..tokenization_utils_base import PreTrainedTokenizerBase
 
 
@@ -278,7 +279,7 @@ class OnnxConfig(ABC):
 
     def generate_dummy_inputs(
         self,
-        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
+        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"],
         batch_size: int = -1,
         seq_length: int = -1,
         num_choices: int = -1,
@@ -296,7 +297,7 @@ class OnnxConfig(ABC):
         Generate inputs to provide to the ONNX exporter for the specific framework
 
         Args:
-            preprocessor: ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
+            preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
                 The preprocessor associated with this model configuration.
             batch_size (`int`, *optional*, defaults to -1):
                 The batch size to export the model for (-1 means dynamic axis).
@@ -325,6 +326,7 @@ class OnnxConfig(ABC):
             Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
         """
         from ..feature_extraction_utils import FeatureExtractionMixin
+        from ..image_processing_utils import ImageProcessingMixin
         from ..tokenization_utils_base import PreTrainedTokenizerBase
 
         if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
@@ -368,6 +370,16 @@ class OnnxConfig(ABC):
                     tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)]
                 return dict(tokenized_input.convert_to_tensors(tensor_type=framework))
             return dict(preprocessor(dummy_input, return_tensors=framework))
+        elif isinstance(preprocessor, ImageProcessingMixin):
+            if preprocessor.model_input_names[0] != "pixel_values":
+                raise ValueError(
+                    f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects"
+                    f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}'
+                )
+            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
+            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
+            return dict(preprocessor(images=dummy_input, return_tensors=framework))
         elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)