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Published 20 hours ago • verified publisher icon aws

YOLOv5 transfer learning and hyperparameter optimisation using Amazon SageMaker

Open OmarKhayyam opened this issue 3 years ago • 29 comments

Issue #, if available: None.

Description of changes: Added Jupyter notebook, and other artifacts (helper code, and sample output) that enable the use of Amazon SageMaker for training YOLOv5 on a custom dataset, and also enables hyperparameter optimization.

Testing done: Ran the notebook on a SageMaker Notebook instance and Studio environment

Merge Checklist

Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.

  • [x] I have read the CONTRIBUTING doc and adhered to the example notebook best practices
  • [x] I have updated any necessary documentation, including READMEs
  • [x] I have tested my notebook(s) and ensured it runs end-to-end
  • [x] I have linted my notebook(s) and code using black-nb -l 100 {path}/{notebook-name}.ipynb

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

OmarKhayyam avatar Sep 14 '22 08:09 OmarKhayyam

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review-notebook-app[bot] avatar Sep 14 '22 08:09 review-notebook-app[bot]

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