A neural network implementation for classifying Different types of Garments on Fashion MNIST dataset using PyTorch.
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
- Epochs: 5
- Batch Size: 10
- Optimizer: Adam (learning rate = 0.001)
- Loss Function: Cross Entropy Loss
- Accuracy: 90.1%
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).
- PyTorch
- torchvision
- numpy
Run the Jupyter notebook to train the model on the Fashion MNIST dataset.