The Fashion-MNIST is a dataset of Zalando’s article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (0-9), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.

• FNN: Mean Squared Error.
• CNN: Cross Entropy.

Implemented a fully connected feedforward neural network (FNN) and a convolutional neural network (CNN) for the classification task of recognizing an image and identifying it as one of the ten possible classes in Fashion-MNIST clothing images dataset using the PyTorch library. Both the networks were trained for exactly 90 epochs.

To view the network architecture, check the classes folder. The trained models have been saved in the models folder, where the code for training the models can also be found.

The files plot_cnn and plot_fnn show the plots of loss vs the number of epochs for the respective networks.

• FNN: Accuracy of 90.33 % on the test set.
• CNN: Accuracy of 91.07 % on the test set.