Fix spelling (#18827)

docs/en/guides/kfold-cross-validation.md

@@ -82,8 +82,8 @@ Without further ado, let's dive in!
     ```python
     import pandas as pd
 
-    indx = [label.stem for label in labels]  # uses base filename as ID (no extension)
-    labels_df = pd.DataFrame([], columns=cls_idx, index=indx)
+    index = [label.stem for label in labels]  # uses base filename as ID (no extension)
+    labels_df = pd.DataFrame([], columns=cls_idx, index=index)
     ```
 
 5. Count the instances of each class-label present in the annotation files.
@@ -146,11 +146,11 @@ The rows index the label files, each corresponding to an image in your dataset,
 
     ```python
     folds = [f"split_{n}" for n in range(1, ksplit + 1)]
-    folds_df = pd.DataFrame(index=indx, columns=folds)
+    folds_df = pd.DataFrame(index=index, columns=folds)
 
-    for idx, (train, val) in enumerate(kfolds, start=1):
-        folds_df[f"split_{idx}"].loc[labels_df.iloc[train].index] = "train"
-        folds_df[f"split_{idx}"].loc[labels_df.iloc[val].index] = "val"
+    for i, (train, val) in enumerate(kfolds, start=1):
+        folds_df[f"split_{i}"].loc[labels_df.iloc[train].index] = "train"
+        folds_df[f"split_{i}"].loc[labels_df.iloc[val].index] = "val"
     ```
 
 3. Now we will calculate the distribution of class labels for each fold as a ratio of the classes present in `val` to those present in `train`.

docs/en/guides/raspberry-pi.md

@@ -95,7 +95,7 @@ Here we will install Ultralytics package on the Raspberry Pi with optional depen
 
 ## Use NCNN on Raspberry Pi
 
-Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn/) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefor our recommendation is to use NCNN with Raspberry Pi.
+Out of all the model export formats supported by Ultralytics, [NCNN](https://docs.ultralytics.com/integrations/ncnn/) delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture).
 
 ## Convert Model to NCNN and Run Inference
 

docs/en/guides/triton-inference-server.md

@@ -48,7 +48,7 @@ from ultralytics import YOLO
 # Load a model
 model = YOLO("yolo11n.pt")  # load an official model
 
-# Retreive metadata during export
+# Retrieve metadata during export
 metadata = []