Update Minimum Reproducible Example (MRE) Docs page (#13443)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/help/minimum_reproducible_example.md

@@ -6,19 +6,19 @@ keywords: Ultralytics, YOLO, Minimum Reproducible Example, MRE, bug report, issu
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories
 
-When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) (MRE). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.
+When submitting a bug report for [Ultralytics](https://ultralytics.com) [YOLO](https://github.com/ultralytics) repositories, it's essential to provide a [Minimum Reproducible Example (MRE)](https://stackoverflow.com/help/minimal-reproducible-example). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.
 
 ## 1. Isolate the Problem
 
-The first step in creating an MRE is to isolate the problem. This means removing any unnecessary code or dependencies that are not directly related to the issue. Focus on the specific part of the code that is causing the problem and remove any irrelevant code.
+The first step in creating an MRE is to isolate the problem. Remove any unnecessary code or dependencies that are not directly related to the issue. Focus on the specific part of the code that is causing the problem and eliminate any irrelevant sections.
 
 ## 2. Use Public Models and Datasets
 
-When creating an MRE, use publicly available models and datasets to reproduce the issue. For example, use the 'yolov8n.pt' model and the 'coco8.yaml' dataset. This ensures that the maintainers and contributors can easily run your example and investigate the problem without needing access to proprietary data or custom models.
+When creating an MRE, use publicly available models and datasets to reproduce the issue. For example, use the `yolov8n.pt` model and the `coco8.yaml` dataset. This ensures that the maintainers and contributors can easily run your example and investigate the problem without needing access to proprietary data or custom models.
 
 ## 3. Include All Necessary Dependencies
 
-Make sure to include all the necessary dependencies in your MRE. If your code relies on external libraries, specify the required packages and their versions. Ideally, provide a `requirements.txt` file or list the dependencies in your bug report.
+Ensure all necessary dependencies are included in your MRE. If your code relies on external libraries, specify the required packages and their versions. Ideally, list the dependencies in your bug report using `yolo checks` if you have `ultralytics` installed or `pip list` for other tools.
 
 ## 4. Write a Clear Description of the Issue
 
@@ -26,13 +26,13 @@ Provide a clear and concise description of the issue you're experiencing. Explai
 
 ## 5. Format Your Code Properly
 
-When submitting an MRE, format your code properly using code blocks in the issue description. This makes it easier for others to read and understand your code. In GitHub, you can create a code block by wrapping your code with triple backticks (\```) and specifying the language:
+Format your code properly using code blocks in the issue description. This makes it easier for others to read and understand your code. In GitHub, you can create a code block by wrapping your code with triple backticks (\```) and specifying the language:
 
-<pre>
+````bash
 ```python
 # Your Python code goes here
 ```
-</pre>
+````
 
 ## 6. Test Your MRE
 
@@ -44,7 +44,7 @@ Here's an example of an MRE for a hypothetical bug report:
 
 **Bug description:**
 
-When running the `detect.py` script on the sample image from the 'coco8.yaml' dataset, I get an error related to the dimensions of the input tensor.
+When running inference on a 0-channel image, I get an error related to the dimensions of the input tensor.
 
 **MRE:**
 
@@ -70,9 +70,9 @@ RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 chann
 
 **Dependencies:**
 
-- torch==2.0.0
-- ultralytics==8.0.90
+- `torch==2.3.0`
+- `ultralytics==8.2.0`
 
-In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model ('yolov8n.pt'), includes all necessary dependencies, and provides a clear description of the problem along with the error message.
+In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model (`"yolov8n.pt"`), includes all necessary dependencies, and provides a clear description of the problem along with the error message.
 
-By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
+By following these guidelines, you'll help the maintainers and [contributors](https://github.com/ultralytics/ultralytics/graphs/contributors) of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.