Docs Colab, OBB and typos fixes (#10366)

Co-authored-by: Olivier Louvignes <olivier@mg-crea.com>
Co-authored-by: RainRat <rainrat78@yahoo.ca>

docs/en/guides/coral-edge-tpu-on-raspberry-pi.md

@@ -82,7 +82,7 @@ To use the Edge TPU, you need to convert your model into a compatible format. It
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/model.pt')  # Load a official model or custom model
+        model = YOLO('path/to/model.pt')  # Load an official model or custom model
 
         # Export the model
         model.export(format='edgetpu')
@@ -91,7 +91,7 @@ To use the Edge TPU, you need to convert your model into a compatible format. It
     === "CLI"
 
         ```bash
-        yolo export model=path/to/model.pt format=edgetpu  # Export a official model or custom model
+        yolo export model=path/to/model.pt format=edgetpu  # Export an official model or custom model
         ```
 
 The exported model will be saved in the `<model_name>_saved_model/` folder with the name `<model_name>_full_integer_quant_edgetpu.tflite`.
@@ -108,7 +108,7 @@ After exporting your model, you can run inference with it using the following co
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/edgetpu_model.tflite')  # Load a official model or custom model
+        model = YOLO('path/to/edgetpu_model.tflite')  # Load an official model or custom model
 
         # Run Prediction
         model.predict("path/to/source.png")
@@ -117,7 +117,7 @@ After exporting your model, you can run inference with it using the following co
     === "CLI"
 
         ```bash
-        yolo predict model=path/to/edgetpu_model.tflite source=path/to/source.png  # Load a official model or custom model
+        yolo predict model=path/to/edgetpu_model.tflite source=path/to/source.png  # Load an official model or custom model
         ```
 
 Find comprehensive information on the [Predict](../modes/predict.md) page for full prediction mode details.

docs/en/guides/isolating-segmentation-objects.md

@@ -108,7 +108,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     1. For more info on `c.masks.xy` see [Masks Section from Predict Mode](../modes/predict.md#masks).
 
-    2. Here, the values are cast into `np.int32` for compatibility with `drawContours()` function from OpenCV.
+    2. Here the values are cast into `np.int32` for compatibility with `drawContours()` function from OpenCV.
 
     3. The OpenCV `drawContours()` function expects contours to have a shape of `[N, 1, 2]` expand section below for more details.
 
@@ -145,7 +145,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     ***
 
-5. Next the there are 2 options for how to move forward with the image from this point and a subsequent option for each.
+5. Next there are 2 options for how to move forward with the image from this point and a subsequent option for each.
 
     ### Object Isolation Options
 

docs/en/guides/nvidia-jetson.md

@@ -54,7 +54,7 @@ The first step after getting your hands on an NVIDIA Jetson device is to flash N
 
 The fastest way to get started with Ultralytics YOLOv8 on NVIDIA Jetson is to run with pre-built docker image for Jetson.
 
-Execute the below command to pull the Docker containter and run on Jetson. This is based on [l4t-pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch) docker image which contains PyTorch and Torchvision in a Python3 environment.
+Execute the below command to pull the Docker container and run on Jetson. This is based on [l4t-pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch) docker image which contains PyTorch and Torchvision in a Python3 environment.
 
 ```sh
 t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

docs/en/guides/view-results-in-terminal.md

@@ -10,7 +10,7 @@ keywords: YOLOv8, VSCode, Terminal, Remote Development, Ultralytics, SSH, Object
   <img width="800" src="https://raw.githubusercontent.com/saitoha/libsixel/data/data/sixel.gif" alt="Sixel example of image in Terminal">
 </p>
 
-Image from the the [libsixel](https://saitoha.github.io/libsixel/) website.
+Image from the [libsixel](https://saitoha.github.io/libsixel/) website.
 
 ## Motivation