This project aimed to find the optimal balance between performance and cost for an image classification task. Using the Fashion-MNIST dataset, I experimented with three different Convolutional Neural Network (CNN) architectures, analyzing the trade-offs between their test accuracy and training time. The analysis concluded that while more complex models yielded higher accuracy, a simpler model offered the best value. I also evaluated various cloud deployment options, ultimately recommending Streamlit Cloud as the most efficient choice for this use case.
- Technologies Used: Python, CNNs, Streamlit, Google Cloud