# A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification Source: [A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:55:22.988Z Authors: Jacob Fein-Ashley, Sachini Wickramasinghe, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna Published: 2024-02-01T12:50:48.000Z ## Abstract Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural networks (CNNs). These networks, while powerful, experience high latency due to the number of operations they perform, which can be problematic in real-time applications. Many image classification models are designed to work with both RGB and grayscale datasets, but classifiers that operate solely on grayscale images are less common. Grayscale image classification has critical applications in fields such as medical imaging and SAR ATR. In response, we present a novel grayscale image classification approach using a vectorized view of images. By leveraging the lightweight nature of Multi-Layer Perceptrons (MLPs), we treat images as vectors, simplifying the problem to grayscale image classification. Our approach incorporates a single graph convolutional layer in a batch-wise manner, enhancing accuracy and reducing performance variance. Additionally, we develop a customized accelerator on FPGA for our model, incorporating several optimizations to improve performance. Experimental results on benchmark grayscale image datasets demonstrate the effectiveness of our approach, achieving significantly lower latency (up to $16\\times$ less on MSTAR) and competitive or superior performance compared to state-of-the-art models for SAR ATR and medical image classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on MNIST Digits 61 (test)](): Image Classification; MNIST Digits 61 (test); Accuracy: 98.04. - [Image Classification on Fashion MNIST 66 (test)](): Image Classification; Fashion MNIST 66 (test); Accuracy: 88.09. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()