Datenset

[Polykru]

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

A new dataset for detecting "mistake" patterns is added to the YOLOv5 repository, leveraging the Roboflow platform.

📊 Key Changes

• Dataset Creation: Added a new dataset (BachelorProjekt) hosted on Roboflow with 168 annotated images in YOLOv5 format.
• Annotations: Labels cover objects in train, valid, and test sets for the new project.
• Metadata Added: A data.yaml file for training configuration, including paths, label info, and licensing, creatively licensed under CC BY 4.0.

🎯 Purpose & Impact

• Purpose: Provides a dedicated dataset for error detection and evaluation, potentially for research or prototyping applications.
• Impact:
  • Makes it easier for users to experiment with mistake detection training using readily available labels and configuration.
  • Offers a reproducible setup for training a YOLOv5 model on specified image data, enhancing workflow simplicity.

🎉 Perfect for projects focusing on detecting specific object classes or research setups!

[github-actions]

Thank you for your submission, we really appreciate it. Like many open-source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution. You can sign the CLA by just posting a Pull Request Comment same as the below format.

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I have read the CLA Document and I sign the CLA

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1 out of 2 committers have signed the CLA.
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❌ @polina Kriuchkova
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[UltralyticsAssistant]

👋 Hello @Polykru, thank you for submitting this detailed PR to ultralytics/yolov5 🚀! To help ensure your contribution is reviewed and ready for integration smoothly, here are some steps and guidelines to follow:

• ✅ Define a Purpose: Clearly explain the specific purpose of this dataset addition. While your summary mentions "BachelorProjekt" and its benefits, could you briefly justify how this dataset aligns with YOLOv5's objectives or use cases? Additionally, if there is any related issue, please link to it in the PR description.

• ✅ Ensure CI Checks are Passing: Please confirm that all Ultralytics Continuous Integration (CI) checks pass without errors. CI checks are critical for verifying that contributions integrate seamlessly without introducing regressions. If any checks fail, you may need to address or configure them further.

• ✅ Update Documentation: If this PR requires changes in usage or dependencies (e.g., paths in data.yaml or licensing details regarding the dataset's use), ensure that you update any relevant documentation. Adding supporting context for newer datasets is often appreciated by the community!

• ✅ Dataset Validation: To ensure dataset reliability:

  1. Validate the dataset’s structure - Are all YOLOv5 formatting requirements adhered to? If not yet validated, tools like the built-in utils.datasets can help ensure compliance.
  2. Provide a sample usage for training/evaluation, e.g., python train.py --data data.yaml logs to confirm the dataset is loading as expected.

• ✅ Minimize Repository Modifications: While most of the changes are dataset-related, please double-check that only essential files are included. It looks like metadata files and data.yaml were added—ensure no redundant or unnecessary changes have been made to keep the repository concise.

• ✅ Sign the CLA: If you haven’t already, please confirm you have signed the Contributor License Agreement (CLA) for your contribution to be considered. You can do this by simply commenting on this PR with the statement:
  "I have read the CLA Document and I sign the CLA"

• ✅ License and Attribution: It’s great to see that the dataset includes a license. To avoid any unintentional conflicts, ensure proper attribution is given and clarify if there are any usage limitations.

For additional guidance on submitting to ultralytics/yolov5, kindly review our Contributing Guide. If you have further questions or need clarification, feel free to leave a comment here. An Ultralytics team member will also assist you shortly to evaluate this contribution! 🚀

Thank you again for contributing to the YOLOv5 community! 😊