PyTorch Fashion MNIST Classification

A neural network implementation for classifying Different types of Garments on Fashion MNIST dataset using PyTorch.

Model Architecture

The model consists of the following layers:

• Conv2d Layer: 1 input channel → 16 output channels (3×3 kernel)
• ReLU Activation: Non-linear activation function
• MaxPool2d Layer: 2×2 pooling
• Flatten Layer: Converts 2D feature maps to 1D vector
• Linear Layer: 256 → 10 output classes

Training Configuration

• Epochs: 5
• Batch Size: 10
• Optimizer: Adam (learning rate = 0.001)
• Loss Function: Cross Entropy Loss

Results

• Accuracy: 90.1%

Dataset

Fashion MNIST - A dataset of 28×28 grayscale images of 10 fashion categories (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot).

Requirements

• PyTorch
• torchvision
• numpy

Usage

Run the Jupyter notebook to train the model on the Fashion MNIST dataset.