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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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<img src="https://cdn.comet.ml/img/notebook_logo.png">
# Using Ultralytics YOLOv5 with Comet
# Using Ultralytics YOLO With Comet
Welcome to the guide on integrating [Ultralytics YOLOv5](https://github.com/ultralytics/yolov5) with [Comet](https://www.comet.com/site/)! Comet provides powerful tools for experiment tracking, model management, and visualization, enhancing your [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflow. This document details how to leverage Comet to monitor training, log results, manage datasets, and optimize hyperparameters for your YOLOv5 models.
Welcome to the guide for integrating [Ultralytics YOLO](https://github.com/ultralytics/yolov5) with [Comet](https://www.comet.com/site/)! Comet offers robust experiment tracking, model management, and visualization tools to enhance your [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflow. This guide explains how to leverage Comet for monitoring training, logging results, managing datasets, and optimizing hyperparameters for your YOLO models.
[![Ultralytics Actions](https://github.com/ultralytics/velocity/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/velocity/actions/workflows/format.yml)
[![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics)
[![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/)
[![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://reddit.com/r/ultralytics)
## 🧪 About Comet
[Comet](https://www.comet.com/site/) builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models.
[Comet](https://www.comet.com/site/) provides tools for data scientists, engineers, and teams to accelerate and optimize [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and machine learning models.
Track and visualize model metrics in real-time, save your [hyperparameters](https://docs.ultralytics.com/guides/hyperparameter-tuning/), datasets, and model checkpoints, and visualize your model predictions with Comet Custom Panels! Comet ensures you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes. Find more information in the [Comet Documentation](https://www.comet.com/docs/v2/).
With Comet, you can track and visualize model metrics in real time, save [hyperparameters](https://docs.ultralytics.com/guides/hyperparameter-tuning/), datasets, and model checkpoints, and visualize predictions using Custom Panels. Comet ensures you never lose track of your work and makes sharing results and collaborating across teams seamless. For more details, see the [Comet Documentation](https://www.comet.com/docs/v2/).
## 🚀 Getting Started
Follow these steps to set up Comet for your YOLOv5 projects.
Follow these steps to set up Comet for your YOLO projects.
### Install Comet
Install the necessary [Python package](https://pypi.org/project/comet-ml/) using pip:
Install the [comet_ml Python package](https://pypi.org/project/comet-ml/) using pip:
```shell
pip install comet_ml
@@ -28,42 +33,45 @@ pip install comet_ml
You can configure Comet in two ways:
1. **Environment Variables:** Set your credentials directly in your environment.
1. **Environment Variables:**
Set your credentials directly in your environment.
```shell
export COMET_API_KEY=<Your Comet API Key>
export COMET_PROJECT_NAME=<Your Comet Project Name> # Defaults to 'yolov5' if not set
```
```shell
export COMET_API_KEY=<Your Comet API Key>
export COMET_PROJECT_NAME=<Your Comet Project Name> # Defaults to 'yolov5' if not set
```
Find your API key in your [Comet Account Settings](https://www.comet.com/).
Find your API key in your [Comet Account Settings](https://www.comet.com/site/).
2. **Configuration File:** Create a `.comet.config` file in your working directory with the following content:
```ini
[comet]
api_key=<Your Comet API Key>
project_name=<Your Comet Project Name> # Defaults to 'yolov5' if not set
```
2. **Configuration File:**
Create a `.comet.config` file in your working directory:
```ini
[comet]
api_key=<Your Comet API Key>
project_name=<Your Comet Project Name> # Defaults to 'yolov5' if not set
```
### Run the Training Script
Execute the YOLOv5 [training script](https://docs.ultralytics.com/modes/train/). Comet will automatically start logging your run.
Run the YOLO [training script](https://docs.ultralytics.com/modes/train/). Comet will automatically log your run.
```shell
# Train YOLOv5s on COCO128 for 5 epochs
# Train YOLO on COCO128 for 5 epochs
python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt
```
That's it! Comet automatically logs hyperparameters, command-line arguments, and training/validation metrics. Visualize and analyze your runs in the Comet UI. For more details on training, see the [Ultralytics Training documentation](https://docs.ultralytics.com/modes/train/).
Comet automatically logs hyperparameters, command-line arguments, and training/validation metrics. Visualize and analyze your runs in the Comet UI. For more details, see the [Ultralytics training documentation](https://docs.ultralytics.com/modes/train/).
<img width="1920" alt="Comet UI showing YOLOv5 training metrics" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
<img width="1920" alt="Comet UI showing YOLO training metrics" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
## ✨ Try an Example!
Explore a completed YOLOv5 training run tracked with Comet:
Explore a completed YOLO training run tracked with Comet:
- **[View Example Run on Comet](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_readme)**
Run the example yourself using this [Google Colab](https://colab.research.google.com/) Notebook:
Run the example yourself using this [Google Colab](https://colab.research.google.com/) notebook:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)
@@ -73,20 +81,20 @@ Comet automatically logs the following information by default:
### Metrics
- **Losses:** Box Loss, Object Loss, Classification Loss (Training and Validation).
- **Performance:** [mAP@0.5](https://www.ultralytics.com/glossary/mean-average-precision-map), mAP@0.5:0.95 (Validation). Learn more about these metrics in the [YOLO Performance Metrics guide](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
- **[Precision](https://www.ultralytics.com/glossary/precision) and [Recall](https://www.ultralytics.com/glossary/recall):** Validation data metrics.
- **Losses:** Box Loss, Object Loss, Classification Loss (Training and Validation)
- **Performance:** [mAP@0.5](https://www.ultralytics.com/glossary/mean-average-precision-map), mAP@0.5:0.95 (Validation). Learn more in the [YOLO Performance Metrics guide](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
- **[Precision](https://www.ultralytics.com/glossary/precision) and [Recall](https://www.ultralytics.com/glossary/recall):** Validation data metrics
### Parameters
- **Model Hyperparameters:** Configuration used for the model.
- **Command Line Arguments:** All arguments passed via the [CLI](https://docs.ultralytics.com/usage/cli/).
- **Model Hyperparameters:** Configuration used for the model
- **Command Line Arguments:** All arguments passed via the [CLI](https://docs.ultralytics.com/usage/cli/)
### Visualizations
- **[Confusion Matrix](https://www.ultralytics.com/glossary/confusion-matrix):** Model predictions on validation data, useful for understanding classification performance ([Wikipedia definition](https://en.wikipedia.org/wiki/Confusion_matrix)).
- **Curves:** PR and F1 curves across all classes.
- **Label Correlogram:** Correlation visualization of class labels.
- **[Confusion Matrix](https://www.ultralytics.com/glossary/confusion-matrix):** Model predictions on validation data ([Wikipedia](https://en.wikipedia.org/wiki/Confusion_matrix))
- **Curves:** PR and F1 curves across all classes
- **Label Correlogram:** Correlation visualization of class labels
## ⚙️ Advanced Configuration
@@ -104,11 +112,11 @@ export COMET_LOG_BATCH_LEVEL_METRICS=true # Log training metrics per batch. Defa
export COMET_LOG_PREDICTIONS=true # Disable prediction logging if set to false. Default: true
```
Refer to the [Comet documentation](https://www.comet.com/docs/v2/) for more configuration options.
For more configuration options, see the [Comet documentation](https://www.comet.com/docs/v2/).
### Logging Checkpoints with Comet
### Logging Checkpoints With Comet
Model checkpoint logging to Comet is disabled by default. Enable it using the `--save-period` argument during training. This saves checkpoints to Comet at the specified epoch interval.
Model checkpoint logging to Comet is disabled by default. Enable it using the `--save-period` argument during training to save checkpoints at the specified epoch interval.
```shell
python train.py \
@@ -120,15 +128,15 @@ python train.py \
--save-period 1 # Save checkpoint every epoch
```
Checkpoints will appear in the "Assets & Artifacts" tab of your Comet experiment. Learn more about model management in the [Comet Model Registry documentation](https://www.comet.com/docs/v2/guides/model-registry/).
Checkpoints will appear in the "Assets & Artifacts" tab of your Comet experiment. Learn more about model management in the [Comet Model Registry documentation](https://www.comet.com/docs/v2/guides/model-registry/using-model-registry/).
### Logging Model Predictions
By default, model predictions (images, ground truth labels, [bounding boxes](https://www.ultralytics.com/glossary/bounding-box)) for the validation set are logged. Control the logging frequency using the `--bbox_interval` argument, which specifies logging every Nth batch per epoch.
**Note:** The YOLOv5 validation dataloader defaults to a batch size of 32. Adjust `--bbox_interval` accordingly.
**Note:** The YOLO validation dataloader defaults to a batch size of 32. Adjust `--bbox_interval` as needed.
Visualize predictions using Comet's Object Detection Custom Panel. See an [example project using the Panel here](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github_readme).
Visualize predictions using Comet's Object Detection Custom Panel. See an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github_readme).
```shell
python train.py \
@@ -167,13 +175,13 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
--weights yolov5s.pt
```
## 💾 Dataset Management with Comet Artifacts
## 💾 Dataset Management With Comet Artifacts
Use [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/artifacts/) to version, store, and manage your datasets.
Use [Comet Artifacts](https://www.comet.com/docs/v2/guides/artifacts/using-artifacts/) to version, store, and manage your datasets.
### Uploading a Dataset
Upload your dataset using the `--upload_dataset` flag. Ensure your dataset follows the structure described in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/) and the dataset config [YAML](https://www.ultralytics.com/glossary/yaml) file matches the format of `coco128.yaml` (see the [COCO128 dataset docs](https://docs.ultralytics.com/datasets/detect/coco128/)).
Upload your dataset using the `--upload_dataset` flag. Ensure your dataset follows the structure described in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/) and that your dataset config [YAML](https://www.ultralytics.com/glossary/yaml) file matches the format of `coco128.yaml` (see the [COCO128 dataset docs](https://docs.ultralytics.com/datasets/detect/coco128/)).
```shell
python train.py \
@@ -191,12 +199,12 @@ View the uploaded dataset in the Artifacts tab of your Comet Workspace.
Preview data directly in the Comet UI.
<img width="1082" alt="Comet UI previewing dataset images" src="https://user-images.githubusercontent.com/7529846/186929215-432c36a9-c109-4eb0-944b-84c2786590d6.png">
Artifacts are versioned and support metadata. Comet automatically logs metadata from your dataset `yaml` file.
Artifacts are versioned and support metadata. Comet automatically logs metadata from your dataset YAML file.
<img width="963" alt="Comet Artifact metadata view" src="https://user-images.githubusercontent.com/7529846/186929256-9d44d6eb-1a19-42de-889a-bcbca3018f2e.png">
### Using a Saved Artifact
To use a dataset stored in Comet Artifacts, update the `path` in your dataset `yaml` file to the Artifact resource URL:
To use a dataset stored in Comet Artifacts, update the `path` in your dataset YAML file to the Artifact resource URL:
```yaml
# contents of artifact.yaml
@@ -223,9 +231,9 @@ Artifacts track data lineage, showing which experiments used specific dataset ve
## 🔄 Resuming Training Runs
If a training run is interrupted (e.g., due to connection issues), you can resume it using the `--resume` flag with the Comet Run Path (`comet://<your_workspace>/<your_project>/<experiment_id>`).
If a training run is interrupted (for example, due to connection issues), you can resume it using the `--resume` flag with the Comet Run Path (`comet://<your_workspace>/<your_project>/<experiment_id>`).
This restores the model state, hyperparameters, arguments, and downloads necessary Artifacts, continuing logging to the existing Comet Experiment. Learn more about [resuming runs in the Comet documentation](https://www.comet.com/docs/v2/guides/experiment-logging/resume-experiment/).
This restores the model state, hyperparameters, arguments, and downloads necessary Artifacts, continuing logging to the existing Comet Experiment. Learn more about [resuming runs in the Comet documentation](https://www.comet.com/docs/v2/guides/experiment-management/resume-experiment/).
```shell
python train.py \
@@ -234,7 +242,7 @@ python train.py \
## 🔍 Hyperparameter Optimization (HPO)
YOLOv5 integrates with the [Comet Optimizer](https://www.comet.com/docs/v2/guides/hyperparameter-optimization/) for easy hyperparameter sweeps and visualization. This helps in finding the best set of parameters for your model, a process often referred to as [Hyperparameter Tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/).
YOLO integrates with the [Comet Optimizer](https://www.comet.com/docs/v2/guides/optimizer/configure-optimizer/) for easy hyperparameter sweeps and visualization. This helps you find the best set of parameters for your model, a process often referred to as [Hyperparameter Tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/).
### Configuring an Optimizer Sweep
@@ -269,10 +277,10 @@ Replace `<num_workers>` with the desired number of parallel processes.
### Visualizing HPO Results
Comet offers various visualizations for analyzing sweep results, such as parallel coordinate plots and parameter importance plots. Explore 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_readme).
Comet offers various visualizations for analyzing sweep results, such as parallel coordinate plots and parameter importance plots. Explore a [project with a completed sweep](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_readme).
<img width="1626" alt="Comet HPO visualization" src="https://user-images.githubusercontent.com/7529846/186914869-7dc1de14-583f-4323-967b-c9a66a29e495.png">
## 🤝 Contributing
Contributions to enhance the YOLOv5-Comet integration are welcome! Please see the [Ultralytics Contributing Guide](https://docs.ultralytics.com/help/contributing/) for more information on how to get involved. Thank you for helping improve this integration!
Contributions to enhance the YOLO-Comet integration are welcome! Please see the [Ultralytics Contributing Guide](https://docs.ultralytics.com/help/contributing/) for more information on how to get involved. Thank you for helping improve this integration!