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@@ -37,21 +37,23 @@ This is an **image-classification / backbone** checkpoint (`RegNetImageClassify`
 
 ```python
 import os
+
 os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
 
 from PIL import Image
-import numpy as np
-from zeromodels.models.regnet import RegNetImageClassify, RegNetModel
+from zeromodels.models.regnet import RegNetImageClassify, RegNetModel, RegNetImageProcessor
 
 model = RegNetImageClassify.from_weights("zeromodels/regnet-y-064")
-backbone = RegNetModel.from_weights("zeromodels/regnet-y-064", as_backbone=True)
+processor = RegNetImageProcessor.from_weights("zeromodels/regnet-y-064")
 
 image = Image.open("your_image.jpg").convert("RGB")
-image = image.resize((224, 224))
-x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
-print(model(x).shape)  # (1, num_classes)
-feats = backbone(x)
-print(len(feats), [tuple(f.shape) for f in feats])
+pixels = processor(image)  # resize + normalize (normalization lives in the processor)
+logits = model(pixels, training=False)
+print(logits.shape)  # (1, num_classes)
+
+# Feature extraction: the backbone without the classifier head
+backbone = RegNetModel.from_weights("zeromodels/regnet-y-064", as_backbone=True)
+features = backbone(pixels, training=False)
 ```
 
 Load any RegNet variant the same way with `from_weights("zeromodels/<variant>")`: