Update requirements.txt

[Powerlax]

Added dill package, needed for loading custom models in detect.py

🤖 Generated by Copilot at c94dc81

Summary

Walkthrough

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

📊 Key Changes

• Addition of dill>=0.3.7 to the requirements.txt file.

🎯 Purpose & Impact

• The new requirement, dill, is likely introduced for advanced data serialization. 🔄
• Users might experience improvements in data handling or features requiring serialization.
• It ensures that users have the correct version of dill installed to avoid potential compatibility issues. ✅

🌟 Summary

Included dill library in requirements for enhanced data serialization support. 🆕

[github-actions]

👋 Hello @Powerlax, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

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• ✅ Reduce changes to the absolute minimum required for your bug fix or feature addition. "It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is." — Bruce Lee

[glenn-jocher]

@Powerlax thanks for the suggestion! We appreciate your contribution to YOLOv5. Your update to requirements.txt has been duly noted and we'll make sure to review it thoroughly. Keep an eye on the repo for further updates! If you need any further assistance, feel free to reach out.

[Powerlax]

Hi @glenn-jocher, any updates on the review?

[glenn-jocher]

Thanks for your patience, @Powerlax! We're currently reviewing the changes, and we'll get back to you as soon as possible. If you have any other questions or need further assistance, feel free to let us know. Your contribution is appreciated!

[github-actions]

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

[Powerlax]

I have read the CLA Document and I sign the CLA

[Powerlax]

Hello @glenn-jocher, any updates on the review?

[glenn-jocher]

Hi @Powerlax,

Thank you for your patience. We are currently in the process of reviewing your pull request. To ensure a smooth review process, could you please confirm that you have tested the addition of the dill package with the latest versions of torch and the YOLOv5 repository? This helps us verify compatibility and functionality.

If you haven't done so already, please provide a minimum reproducible code example that demonstrates the necessity of the dill package for loading custom models in detect.py. You can refer to our Minimum Reproducible Example Guide for more details on how to create one.

Your contribution is valuable to us, and we want to ensure that everything works seamlessly for all users. Looking forward to your response!

[Powerlax]

Hi @glenn-jocher, thank you for getting back to me.

I have tested this with the latest version of the torch library (2.3). Training and Running an object detection model works.

The issue that the dill package is attempting to solve is that when running a custom model (from a locally stored .pt file), the dill module is needed to open the file. If the dill module is not installed, then it gives you an error telling you to install it (ModuleNotFound error). By installing it with the rest of the modules in requirements.txt, it makes it easier for developers to use YOLOv5 with their custom model when they open a new jupyter notebook or virtual environment.

[glenn-jocher]

Hi @Powerlax,

Thank you for the detailed explanation and for testing with the latest version of the torch library. Your insights are very helpful!

To ensure we can fully understand and address the issue, could you please provide a minimum reproducible code example that demonstrates the necessity of the dill package for loading custom models in detect.py? This will help us verify the problem and ensure that our solution works seamlessly for all users. You can refer to our Minimum Reproducible Example Guide for more details on how to create one.

Here's a quick example of what the code snippet might look like:

import torch
import dill

# Example of loading a custom model
model_path = 'path/to/your/custom_model.pt'
model = torch.load(model_path, pickle_module=dill)

# Run detection
results = model('path/to/your/image.jpg')
results.show()

This will help us reproduce the issue on our end and validate the necessity of adding dill to the requirements.

Thank you again for your contribution and for helping improve YOLOv5! If you have any further questions or need additional assistance, feel free to reach out. 😊