The deep learning model, which uses Small VGGNet architecture to focus on image classification, performed exceptionally well, correctly categorizing the images. Google Photos has already implemented ...

This repository contains a comprehensive implementation of the ResNet-50 architecture, a powerful deep learning model widely used for image classification tasks. ResNet-50, part of the Residual ...

Nowadays, deep learning has got a major success in computer vision, especially in image recognition. In this paper, a new architecture based on DenseNets which is referred to as Multi-Scale Input ...

Deep learning library KERAS was employed and MobileNet architecture was fine-tuned for image classification task. Topics python machine-learning deep-learning detection jupyter-notebook ...

Detection of “indeterministic” defect types such as cracks and/or scratches is quite challenging since such defects may have a variety of shapes, locations and severity. Deep learning, a subfield of ...

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ...

Most of the advances in neural network models, for example in image classification and language translation, have required considerable hand-tuning of the neural network architecture, which is ...

New computing architecture: Deep learning with light ... 98.7 percent for image classification and 98.8 percent for digit recognition -- at rapid speeds. "We had to do some calibration, ...

Vision Transformer (ViT): A deep learning architecture that applies transformer models—originally developed for language processing—to the task of image classification, offering an alternative ...

Training deep learning models without using GPUs can be the difference between waiting a few minutes to waiting hours. Automated Text Classification In order to build predictive models, we need ...