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README.md

@@ -25,35 +25,7 @@ Lineage: EfficientNetV2 -> miewid-msv3 -> miewid-msv3-latonia-1233.
 Images are preprocessed as described in the preprint:
 - Rotate images so the head is oriented upwards.
 - Detect a bounding box using MegaDetector and crop to the bbox.
-- Apply the following transforms:
-
-```python
-from torchvision import transforms
-
-preprocess = transforms.Compose([
-    ZoomCenterCrop(zoom=2.0),
-    transforms.Resize((440, 440)),
-    transforms.ToTensor(),
-    transforms.Normalize(
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225]),
-])
-```
-
-`ZoomCenterCrop` implementation:
-
-```python
-class ZoomCenterCrop:
-    def __init__(self, zoom=1.0):
-        self.zoom = zoom
-
-    def __call__(self, img):
-        w, h = img.size
-        m = int(min(h, w) / self.zoom)
-        left = (w - m) // 2
-        top = (h - m) // 2
-        return img.crop((left, top, left + m, top + m))
-```
+- Apply the transforms shown in the Usage example below.
 
 ## Training
 See the preprint for full training details and evaluation protocol.