CNN for Fashion MNIST Dataset

This repository uses PyTorch, NumPy, Matplotlib.

Description

This deep learning image classification model that uses convolutional neural networks (CNNs) to identify different types of clothing items in images. The model is trained on the Fashion-MNIST dataset, which consists of 60,000 grayscale images of 10 different clothing categories, and evaluated on a test set of 10,000 images.

The final accuracy achieved by the model is reported, indicating how well the model is able to classify clothing items. Check out the script here.

Steps | Learnings

1. Load and preprocess the FashionMNIST dataset
2. Define the architecture of the convolutional neural network (CNN)
3. Train the CNN on the FashionMNIST dataset
4. Evaluate the performance of the trained CNN on a separate test set
5. Make predictions on new, unseen images using the trained CNN.

License

This script is open-source and licensed under the MIT License. For more details, check the LICENSE file.