Question 1

How do I add a new model to BeautyNet?

Accepted Answer

To add a new model, extend the base classes in the template, implementing your architecture within the object-oriented structure. Due to limited documentation, rely on PyTorch knowledge for customization, but the design promotes clarity.

Question 2

Is BeautyNet better than PyTorch Lightning for organizing projects?

Accepted Answer

BeautyNet focuses on code structure and quality without framework overhead, while PyTorch Lightning offers more features like automated training loops. Choose BeautyNet for simplicity and control, PyTorch Lightning for built-in functionalities and faster setup.

Question 3

Does BeautyNet support GPU training?

Accepted Answer

Yes, since it's built on PyTorch, you can use GPU training by configuring PyTorch's device settings. However, the template doesn't include specific optimizations; you need to handle GPU integration in your code.

Question 4

Can I use BeautyNet for image classification tasks?

Accepted Answer

Absolutely, BeautyNet is flexible and can be adapted for image classification by implementing custom dataset and model classes. Integrate libraries like torchvision for data loading, but the template provides only the structural foundation.

Question 5

What are the prerequisites for installing BeautyNet?

Accepted Answer

You need Python 3 and PyTorch installed, as the setup.py script handles dependencies. Ensure your environment meets PyTorch's requirements, but no heavy additional dependencies are specified, keeping it lightweight.

Question 6

How to handle data loading in BeautyNet?

Accepted Answer

Data loading is not pre-implemented; you must create custom dataset classes following PyTorch's DataLoader patterns. The template offers a structure to organize these components, but implementation is left to the user.

BeautyNet

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