Convolutional Neural Networks (CNNs) are highly effective for image classification but often require significant computational resources, making them difficult to deploy on low-power devices. This project introduces Mini Color NN, a novel approach that combines k-means vector quantization and Floyd-Steinberg dithering to reduce the color complexity of images in the CIFAR-10 dataset while preserving critical visual information. The resulting Mini Color CIFAR-10 dataset reduces storage requirements by 1.7× and accelerates training time by 1.3×, with only a minor drop in classification accuracy (from 92.88% to 92.01%). These results demonstrate that significant computational efficiency can be achieved without compromising CNN performance, supporting broader deployment of deep learning models in edge computing and resource-constrained environments. This work contributes a publicly available, lightweight dataset and offers a practical preprocessing strategy for efficient image-based machine learning.
After cloning and cd into the repository, run CIFAR10-CNN.py and wait a few minutes for training to begin. To use the Original Dataset, set DATA_PATH to "cifar-10-batches-py" or "cifar-10-custom" for the Mini Color Version.
Quit the program at any time to view training progress across epochs. To resume, run the Python file again and follow instructions in the terminal.