Added example to mixeup docstring

albumentations/augmentations/mixing/transforms.py

@@ -37,7 +37,7 @@ class MixUp(ReferenceBasedTransform):
                 - The returned dictionary must include an 'image' key with a numpy array value.
                 - It may also include 'mask', 'global_label' each associated with numpy array values.
             Defaults to a function that assumes input dictionary contains numpy arrays and directly returns it.
-         mix_coef_return_name (str): Name used for the applied alpha coefficient in the returned dictionary.
+        mix_coef_return_name (str): Name used for the applied alpha coefficient in the returned dictionary.
             Defaults to "mix_coef".
         alpha (float):
             The alpha parameter for the Beta distribution, influencing the mix's balance. Must be ≥ 0.
@@ -58,6 +58,30 @@ class MixUp(ReferenceBasedTransform):
     Notes:
         - If no reference data is provided, a warning is issued, and the transform acts as a no-op.
         - Notes if images are in float32 format, they should be within [0, 1] range.
+
+    Example Usage:
+        import albumentations as A
+        import numpy as np
+        from albumentations.core.types import ReferenceImage
+
+        # Prepare reference data
+        reference_data = [ReferenceImage(image=np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8),
+                                         mask=np.random.randint(0, 4, (100, 100, 1), dtype=np.uint8),
+                                         global_label=np.random.choice([0, 1], size=3)) for i in range(10)]
+
+        aug = A.Compose([A.RandomRotate90(), A.MixUp(p=1, reference_data=reference_data, read_fn=lambda x: x)])
+
+        # Apply augmentations
+        image = np.empty([100, 100, 3], dtype=np.uint8)
+        mask = np.empty([100, 100], dtype=np.uint8)
+        global_label = np.array([0, 1, 0])
+        data = aug(image=image, global_label=global_label, mask=mask)
+        transformed_image = data["image"]
+        transformed_mask = data["mask"]
+        transformed_global_label = data["global_label"]
+
+        # Print applied mix coefficient
+        print(data["mix_coef"])  # Output: e.g., 0.9991580344142427
     """
 
     _targets = (Targets.IMAGE, Targets.MASK, Targets.GLOBAL_LABEL)