Spaces:
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README.md
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---
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license: afl-3.0
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title: Spaces Readme
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sdk: gradio
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emoji: 🚀
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colorFrom: yellow
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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# Lighting DavidNet over Spaces
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### Gradio Integration for Interactive Model Predictions:
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Develop a Gradio interface that envelops the model for seamless interactive predictions. This setup includes the following user-driven functionalities:
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**GradCAM Visualizations**:
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- Users can opt to visualize GradCAM images.
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- Flexibility to choose the number of GradCAM images for display.
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- Selection of the specific layer for generating GradCAM visualizations.
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- Ability to adjust opacity of the overlaid GradCAM images.
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**Misclassified Images**:
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- Users have the choice to review misclassified images.
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- Option to determine the quantity of misclassified images to be presented.
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**Image Upload**:
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- Users can upload their own images for predictions.
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- A set of 10 example images is available for experimentation.
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**Class Preferences**:
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- Users can specify the number of top predicted classes they want to see.
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- A limit of 10 classes ensures a manageable display.
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### Deployment on Hugging Face Spaces:
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The Gradio application, featuring the integrated model, is deployed on Hugging Face Spaces. The Spaces README encompasses the following elements:
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- A comprehensive overview of the Spaces app's functionality.
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- Exclusion of any training-related code from the README.
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- Inclusion of links to the Lightning codebase on GitHub, ensuring a clear separation of model training specifics from deployment details.
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### GitHub Repository for Lightning Training Code:
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The Lightning training code resides in a distinct GitHub repository. The repository's detailed README incorporates:
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- A comprehensive log detailing the training progression.
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- Graphs illustrating the training epochs' loss function.
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- Showcasing of 10 instances of misclassified images, complete with actual and predicted labels.
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---
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