Add tqdm progress bar for image datasets and improve CLI help in detect.py #IEEESOC

[U-m-4r]

This PR enhances the user experience of [detect.py] by:

• Adding a progress bar (via tqdm) when processing image datasets, making it easier to monitor inference progress on large sets of images.

• Improving the command-line interface with a descriptive description and epilog in the argument parser, including a clear usage example.
• All changes are backward compatible and do not affect detection logic or outputs.
• These improvements make the script more user-friendly and approachable, especially for new users and those working with large datasets.

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

This update adds a progress bar for image processing and improves the command-line help for YOLOv5 detection, making the tool more user-friendly. 🚀

📊 Key Changes

• Integrated a progress bar (using tqdm) when processing images, showing real-time progress.
• Enhanced the command-line interface with a description and usage example for easier understanding.

🎯 Purpose & Impact

• Better User Experience: Users now see a visual progress bar when running detection on images, making it easier to track progress. ⏳
• Improved Usability: Clearer command-line help and examples make it simpler for new and existing users to understand how to use the detection script. 📝
• No Impact on Results: These changes do not affect detection accuracy or performance, only the user interface and experience.

[github-actions]

All Contributors have signed the CLA. ✅
_{Posted by the CLA Assistant Lite bot.}

[UltralyticsAssistant]

👋 Hello @U-m-4r, thank you for submitting an ultralytics/yolov5 🚀 PR! This is an automated response to help streamline the review process. An Ultralytics engineer will also assist you soon.

Please review the following checklist to ensure a smooth integration of your enhancements:

• ✅ Define a Purpose: Clearly explain the purpose of your improvements in your PR description and link any relevant issues. Make sure your commit messages are clear and follow project conventions.
• ✅ Synchronize with Source: Confirm your PR is up to date with the ultralytics/yolov5 main branch. If not, use the 'Update branch' button or run git pull and git merge main locally.
• ✅ Ensure CI Checks Pass: Check that all Ultralytics Continuous Integration (CI) checks are passing. If any checks fail, please address them.
• ✅ Update Documentation: Update the relevant documentation for any new or modified features.
• ✅ Add Tests: If applicable, add or update tests for your changes and verify that all tests pass.
• ✅ Sign the CLA: Ensure you have signed the Contributor License Agreement (CLA) if this is your first PR by commenting "I have read the CLA Document and I sign the CLA" below.
• ✅ Minimize Changes: Limit your changes to the minimum necessary for your feature addition or bug fix. "It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is." — Bruce Lee

For more details, please refer to our Contributing Guide. If you have any questions, feel free to comment here. Thanks for helping make YOLOv5 even better! 🎉🛠️

[U-m-4r]

I have read the CLA Document and I sign the CLA

[U-m-4r]

@glenn-jocher At least 1 approving review is required :)

[U-m-4r]

@glenn-jocher kindly review...

[github-actions]

👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap.

We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved.

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Thank you for your contributions to YOLO 🚀 and Vision AI ⭐