Add HeatMap guide in real-world-projects + Code in Solutions Directory (#6796)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/yolov5/tutorials/clearml_logging_integration.md

@@ -38,13 +38,13 @@ To keep track of your experiments and/or data, ClearML needs to communicate to a
 
 Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
 
-1. Install the `clearml` python package:
+- Install the `clearml` python package:
 
    ```bash
    pip install clearml
    ```
 
-2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
+- Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
 
    ```bash
    clearml-init
@@ -89,15 +89,13 @@ This will capture:
 - Images with bounding boxes per epoch
 - Mosaic per epoch
 - Validation images per epoch
-- ...
+
 
 That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
 
 There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
 
-<br>
-
-## 🔗 Dataset Version Management
+### 🔗 Dataset Version Management
 
 Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
 
@@ -165,7 +163,7 @@ python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_i
 
 <br>
 
-## 👀 Hyperparameter Optimization
+### 👀 Hyperparameter Optimization
 
 Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
 

docs/en/yolov5/tutorials/comet_logging_integration.md

@@ -10,22 +10,22 @@ keywords: YOLOv5, Comet, Machine Learning, Ultralytics, Real time metrics tracki
 
 This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2)
 
-# About Comet
+## About Comet
 
 Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models.
 
 Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)!
 Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
 
-# Getting Started
+## Getting Started
 
-## Install Comet
+### Install Comet
 
 ```shell
 pip install comet_ml
 ```
 
-## Configure Comet Credentials
+### Configure Comet Credentials
 
 There are two ways to configure Comet with YOLOv5.
 
@@ -48,7 +48,7 @@ api_key=<Your Comet API Key>
 project_name=<Your Comet Project Name> # This will default to 'yolov5'
 ```
 
-## Run the Training Script
+### Run the Training Script
 
 ```shell
 # Train YOLOv5s on COCO128 for 5 epochs
@@ -59,7 +59,7 @@ That's it! Comet will automatically log your hyperparameters, command line argum
 
 <img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
 
-# Try out an Example!
+## Try out an Example!
 
 Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
 
@@ -67,7 +67,7 @@ Or better yet, try it out yourself in this Colab Notebook
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
 
-# Log automatically
+## Log automatically
 
 By default, Comet will log the following items
 
@@ -88,7 +88,7 @@ By default, Comet will log the following items
 - Plots for the PR and F1 curves across all classes
 - Correlogram of the Class Labels
 
-# Configure Comet Logging
+## Configure Comet Logging
 
 Comet can be configured to log additional data either through command line flags passed to the training script or through environment variables.
 
@@ -254,7 +254,7 @@ comet optimizer -j <set number of workers> utils/loggers/comet/hpo.py \
   utils/loggers/comet/optimizer_config.json"
 ```
 
-### Visualizing Results
+## Visualizing Results
 
 Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)