---
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

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

For more details on the assignment, refer to github link: 
https://github.com/ToletiSri/TSAI_ERA_Assignments/tree/main/S12

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)


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference