Harnessing Effectiveness of ResNet-50 and EfficientNet for Few-Shot Learning

Abstract

Inspired by the concept of human intelligence- learning and expanded upon with several examples – several- step learning focused on computers that can classify images in a comparable way. This article covers the interesting field of sparse learning, focusing on comparing its implementation using two popular deep learning networks: ResNet and EfficientNet. Little learning has the potential to be effective on tasks where obtaining large data sets is expensive or impossible. This allows machines to mimic humans’ ability to learn and expand from small samples, thus opening possibilities in the field of several types of diagnostics, personalized recommendations, systems, and robotics. Our main goal is to measure and compare the accuracy achieved by these models when learning on limited datasets and to show that EfficientNet achieves better accuracy when it requires fewer parameters and computational resources compared to ResNet-We considered VGG-flowers dataset for comparison. Our results show that Narrow EfficientNet outperforms ResNet-50 in terms of overall accuracy (85.20% vs. 84.30%), precision (85.60% vs. 85.40%), recall (85.30% vs. 84.50%) and F1 acquisition (85.45% vs. 84.95%). This suggests that EfficientNet’s emphasis on computational efficiency and parallelism may provide a slight advantage on limited data.