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Is your feature request related to a problem? Please describe.
Researchers often need to experiment with various CNN architectures to improve face recognition performance. Currently, the system lacks flexibility in allowing users to switch between different backbone architectures such as ResNet, EfficientNet, etc.
Describe the solution you'd like
Add support for multiple CNN backbones by creating reusable modules that allow researchers to select and switch between architectures easily.
Describe alternatives you've considered
Manually implementing each CNN backbone architecture for every model, but this approach is time-consuming and error-prone.
Additional context
This will enable efficient comparison between various models. Should also be integrated with UI Support
Checklist
Research various CNN backbones (ResNet, EfficientNet, etc.)
Understand architectural differences, performance, and integration challenges.
Create reusable modules for CNN backbones
Ensure compatibility with existing codebase.
Make the modules easy to switch between backbones.
Implement functionality to choose CNN backbones dynamically
Add functionality for researchers to switch between backbones (via config.yaml files).
Test with different backbones (ResNet, EfficientNet, etc.)
Verify that the models work as expected and provide comparable results
Document how to use different backbones in the system
Add detailed documentation for the new functionality.
The text was updated successfully, but these errors were encountered:
Is your feature request related to a problem? Please describe.
Researchers often need to experiment with various CNN architectures to improve face recognition performance. Currently, the system lacks flexibility in allowing users to switch between different backbone architectures such as ResNet, EfficientNet, etc.
Describe the solution you'd like
Add support for multiple CNN backbones by creating reusable modules that allow researchers to select and switch between architectures easily.
Describe alternatives you've considered
Manually implementing each CNN backbone architecture for every model, but this approach is time-consuming and error-prone.
Additional context
This will enable efficient comparison between various models. Should also be integrated with UI Support
Checklist
Research various CNN backbones (ResNet, EfficientNet, etc.)
Create reusable modules for CNN backbones
Implement functionality to choose CNN backbones dynamically
Test with different backbones (ResNet, EfficientNet, etc.)
Document how to use different backbones in the system
The text was updated successfully, but these errors were encountered: