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

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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>
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Muhammad Rizwan Munawar 2023-12-07 01:39:29 +05:00 • committed by GitHub
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18
docs/en/yolov5/tutorials/comet_logging_integration.md
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@ -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)