ConvNeXt-Tiny for High-Accuracy Natural Scene Image Classification with Test-Time Augmentation

Deep Learning-Based Classification of Military Aircraft Types from Satellite Imagery

Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning ap...

A data efficient pyramid vision transformer for image classification

A novel data efficient pyramid vision transformer (DE-PVT), designed to train on limited datasets by utilizing a teacher-student approach and linear computational complexity relative to the number of patches, achieved through a linear spatial reduction mechanism is introduced.

Vision Transformer Architectures for Next-Generation Image Classification: Attention-Driven Visual Understanding

Vision Transformer (ViT) architectures have emerged as powerful alternatives to conventional convolutional neural networks for image classification because they model long-range visual dependencies through self-attention. This paper presents a software-based image classification framework that uses a pre-trained ViT-Ba...

LLFA-Net: a collaborative CNN-transformer framework for land cover classification using high-resolution remote sensing images

Performance Comparison of ALEXNET, VGG16, and ResNet on CIFAR‑10 Image Classification

Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.

A Hybrid Deep Learning Architecture for Image Classification Across Diverse Visual Recognition Applications

This study trains a hybrid deep learning system to efficiently recognize photos using the Caltech-256 dataset and findings validate the proposed hybrid design's provision of a strong and efficient model.