README.md

---
title: MnistStudio
emoji: 🐨
colorFrom: red
colorTo: indigo
sdk: docker
app_port: 8000
pinned: false
license: mit
short_description: Train and perform inference on MNIST dataset
---
# MNIST Application

## Overview

This is a simple application that can be used to train a convolutional neural network model to classify images of handwritten digits. The same application can also be used to perform inference of the digits drawn by the user.

## Application Description
- The landing page consists of two buttons, one for training the model and one for performing inference.
- On clicking the inference button, a new page is loaded where the user can draw a digit on the canvasand select the model to perform inference. 
- The inference results are displayed on the same page.
- On clicking the training button, a new page is loaded where two buttons are displayed, one for training single model and another for comparing multiple models.
- On clicking the train single model button, a new page is loaded where the user can select following options:
    - Number of kernels of three blocks of the network
    - Optimizer [Admam, SGD]
    - Batch Size [32, 64, 128]
    - Number of Epochs [1, 2, 3]
- Once these parameters are selected, the user can click on the train button to start the training. Training and validation loss, accuracy are displayed on the same page.
- On clicking the train and compare models button, a new page is loaded where the user can select following options for both the models:
    - Number of kernels of three blocks of the network for each model
    - Optimizer [Admam, SGD] for each model
    - Batch Size [32, 64, 128] for each model
    - Number of Epochs [1, 2, 3] for each model
- Once these parameters are selected, the user can click on the train button to start the training. Training and validation loss, accuracy are displayed on the same page.