| --- |
| title: TSAI S12 |
| emoji: ⚡ |
| colorFrom: green |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 3.40.1 |
| app_file: app.py |
| pinned: false |
| license: mit |
| --- |
| |
| # 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). |
| Follow https://theschoolof.ai/ for more updates on TSAI |
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| For more details on the assignment, refer to github link: |
| 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. |
| We have then saved the trained model to file - saved_model.pth. The saved model is uploaded and used in the current Space. |
| |
| As part of the app, we provide provide the following features to the user: |
| - ask the user whether he/she wants to see GradCAM images and how many, and from which layer, allow opacity change as well |
| - allow users to upload new images, as well as provide 10 example images |
| - ask how many top classes are to be shown (make sure the user cannot enter more than 10) |
| |
| The custom resnet model used, has the following model architecture |
| |
| ----------------------------------------------------------- |
| # | Name | Type | Params |
| ------------------------------------------------------------- |
| 0 | loss_criteria | CrossEntropyLoss | 0 |
| 1 | accuracy | MulticlassAccuracy | 0 |
| 2 | convblockPreparation | Sequential | 1.9 K |
| 3 | convblockL1X1 | Sequential | 74.0 K |
| 4 | convblockL1R1 | Sequential | 295 K |
| 5 | convblockL2X1 | Sequential | 295 K |
| 6 | convblockL3X1 | Sequential | 1.2 M |
| 7 | convblockL3R1 | Sequential | 4.7 M |
| 8 | FinalBlock | Sequential | 0 |
| 9 | FC | Sequential | 5.1 K |
| 10 | dropout | Dropout | 0 |
| ------------------------------------------------------------- |
| 6.6 M Trainable params |
| 0 Non-trainable params |
| 6.6 M Total params |
| 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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