Upload processor

image_processing_spice_cnn.py

@@ -14,6 +14,8 @@ from transformers.image_transforms import (
     to_channel_dimension_format,
 )
 from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
     ChannelDimension,
     ImageInput,
     PILImageResampling,
@@ -23,9 +25,6 @@ from transformers.image_utils import (
 )
 from transformers.utils import TensorType
 
-MNIST_STANDARD_MEAN = [0.5]
-MNIST_STANDARD_STD = [0.5]
-
 
 class SpiceCNNImageProcessor(BaseImageProcessor):
     """
@@ -71,7 +70,7 @@ class SpiceCNNImageProcessor(BaseImageProcessor):
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
-        size = size if size is not None else {"height": 28, "width": 28}
+        size = size if size is not None else {"height": 224, "width": 224}
         size = get_size_dict(size)
         self.do_resize = do_resize
         self.do_rescale = do_rescale
@@ -80,9 +79,9 @@ class SpiceCNNImageProcessor(BaseImageProcessor):
         self.resample = resample
         self.rescale_factor = rescale_factor
         self.image_mean = (
-            image_mean if image_mean is not None else MNIST_STANDARD_MEAN
+            image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         )
-        self.image_std = image_std if image_std is not None else MNIST_STANDARD_STD
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
 
     def resize(
         self,

preprocessor_config.json

@@ -6,16 +6,20 @@
   "do_rescale": false,
   "do_resize": false,
   "image_mean": [
+    0.5,
+    0.5,
     0.5
   ],
   "image_processor_type": "SpiceCNNImageProcessor",
   "image_std": [
+    0.5,
+    0.5,
     0.5
   ],
   "resample": 2,
   "rescale_factor": 0.00392156862745098,
   "size": {
-    "height": 28,
-    "width": 28
+    "height": 224,
+    "width": 224
   }
 }