Fashion Mnist Classifier

BHUMIK KUMAR KAPOOR

BHUMIK KUMAR KAPOOR

New Delhi, Delhi

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This project implements a multi-class image classifier for the Fashion MNIST dataset using a Convolutional Neural Network (CNN) and Streamlit for the web interface. Users can upload images to classify them into one of ten fashion categories. ...learn more

Project status: Under Development

Artificial Intelligence

Intel Technologies
Other

Code Samples [1]

Overview / Usage

This project implements a multi-class image classifier for the Fashion MNIST dataset using a Convolutional Neural Network (CNN) and Streamlit for the web interface. Users can upload images to classify them into one of ten fashion categories.

Methodology / Approach

Model Architecture

The CNN architecture consists of the following layers:

  • Convolutional Layer: Applies 16 filters of size 3x3 with ReLU activation and padding.
  • Max Pooling Layer: Reduces dimensionality with a kernel size of 2x2.
  • Fully Connected Layer: Outputs class predictions for the 10 categories in Fashion MNIST.
Image Preprocessing

Uploaded images are preprocessed using the following steps:

  1. Convert the image to grayscale.
  2. Resize the image to 28x28 pixels.
  3. Normalize the pixel values to be between -1 and 1.

Technologies Used

o run this project, you'll need the following Python libraries:

  • streamlit
  • torch
  • torchvision
  • torchmetrics
  • Pillow
  • numpy

You can install the required packages using pip:

pip install streamlit torch torchvision torchmetrics Pillow numpy

Repository

https://github.com/kbhumik27/FASHION-MNIST-CLASSIFIER

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