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Track your YOLOv5 and YOLOv8 runs with Comet’s platform

Get automatic experiment tracking with tools to version datasets, debug and reproduce models, and visualize performance across training runs.

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Visualize, compare, and optimize your YOLOv5 and YOLOv8 runs with Comet’s platform

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Easily track runs from any notebook, IDE, or script
  • Out of the box, Comet automatically logs metrics such as mAP, precision, recall, model hyperparameters, and more
  • Easily log custom data like YOLOv5 predictions, checkpoints, models and datasets
Leverage Comet’s powerful hyper-parameter optimizer
  • Identify which hyper-parameters are having the biggest impact on your performance
  • Generate a Model with full confidence that it is the best
Easily compare experiments and see why they’re different
  • Compare the impact of hyperparameters
  • Visualize bounding box predictions
  • Log interactive confusion matrices
  • Examine system metrics and source code
Upload your custom object detection datasets to track which model was trained on what data
  • Version your data to keep track of new data drops 
  • See what experiments are using your data

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With automatic logging of 10+ popular ML frameworks, Comet’s platform makes it easy to track experiments, evaluate model performance and log model artifacts and datasets.

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Comet integrates with popular ML Frameworks and tools

With Comet, you can log all your models and leverage visualization libraries like Shap and Matplotlib and store them with your models and datasets. All your data in one place.

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Expand your ML workflow with Comet

Whether you’re testing a model or building production-ready applications, Comet gives you full visibility of your ML workflows and helps you with reproducibility of all your models.

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Save and track datasets from training runs to production

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Save, track and document model changes

Model Production Monitoring

Visualize model performance at any scale for observability of the complete ML Lifecycle

An everyday application of YOLOv5

Kristen’s bus project leverages YOLOv5 to notify her family via text message just before the school bus arrives. Because of the unique route the bus takes, there’s about 5 minutes from when it’s detected to when the kids need to be at the stop. Follow along to see how she sets up the whole workflow from end to end.

Part 1 on setup
Part 2 on data labeling
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