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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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+
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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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+