Tagged: GPU Acceleration

Projects

• Brute Forcing Keypoints: BoVW vs CNN — This project investigates whether modern GPU acceleration (via cuML/RAPIDS on H100 architecture) can rehabilitate classical Bag-of-Visual-Words methods for image classification. By scaling codebook construction to 50 million keypoints on CIFAR-10, we benchmark GPU-accelerated BoVW pipelines against modernised CNN architectures (LeNet-5 variant and VGG-16-BN). Our best BoVW configuration achieves 65.46% test accuracy, matching a shallow CNN but falling substantially short of VGG-16-BN at 83.90%. The results confirm that while modern hardware enables previously impractical scaling for classical methods, fundamental limitations of BoVW—particularly vector quantisation error and the absence of spatial hierarchy—remain decisive when compared to deeper architectures.