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README.md
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license: mit
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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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license: mit
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---
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# The School of AI - ERA(Extensive & Reimagined AI Program) - Assignment 12
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This folder consists of Assignment-12 from ERA course offered by - TSAI(The school of AI).
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Follow https://theschoolof.ai/ for more updates on TSAI
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For more details on the assignment, refer to github link:
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https://github.com/ToletiSri/TSAI_ERA_Assignments/tree/main/S12
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As part of the assignment, we have trained a custom resnet model on CIFAR-10 dataset, using pytorch lightning.
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We have then saved the trained model to file - saved_model.pth. The saved model is uploaded and used in the current Space.
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As part of the app, we provide provide the following features to the user:
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- ask the user whether he/she wants to see GradCAM images and how many, and from which layer, allow opacity change as well
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- allow users to upload new images, as well as provide 10 example images
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- ask how many top classes are to be shown (make sure the user cannot enter more than 10)
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The custom resnet model used, has the following model architecture
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-----------------------------------------------------------
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# | Name | Type | Params
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-------------------------------------------------------------
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0 | loss_criteria | CrossEntropyLoss | 0
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1 | accuracy | MulticlassAccuracy | 0
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2 | convblockPreparation | Sequential | 1.9 K
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3 | convblockL1X1 | Sequential | 74.0 K
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4 | convblockL1R1 | Sequential | 295 K
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5 | convblockL2X1 | Sequential | 295 K
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6 | convblockL3X1 | Sequential | 1.2 M
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7 | convblockL3R1 | Sequential | 4.7 M
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8 | FinalBlock | Sequential | 0
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9 | FC | Sequential | 5.1 K
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10 | dropout | Dropout | 0
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-------------------------------------------------------------
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6.6 M Trainable params
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0 Non-trainable params
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6.6 M Total params
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26.293 Total estimated model params size (MB)
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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