# This is a image classification by VGG16 pre-trained model.#

Baseline model for multimodal classification based on images and text. Text representation obtained from pretrained BERT base model and image representation obtained from VGG16 pretrained model.

Image Classification and Image Retrieval on landmarks using transfer learning on VGG16 and DeLF. This is a famous Kaggle competition https://www.kaggle.com/c/landmark-recognition-challenge

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

This project simply implements three common deep learning models (VGG16, ResNet18, ViT) for medical image classification tasks using the PathMNIST dataset.

Main contribution of this repo is thorough evaluation of off-the-shelf approach for image classification based on a feature extraction with a single feed forward pass trough pretrained VGG16.

Transfer learning using tensorflow VGG16 image classification model

Performing a classification of the land-use image provided by a Remote sensing process using a Convolution-Neural-Network trained through pre-trained neural network VGG16 (transfer learning) 🛰️🛰️🛰️

image classification with VGG16+SVM and VGG-16

This is a research project about how to do medical image classification on small dataset by deep learning The pdf is the report. The VGG16 is the code for experiments

Using CNN for classifying 101 different food categories - using VGG16, Alex Net and SVM

This repository presents a comparative analysis of four Convolutional Neural Network (CNN) models—U-Net, ResNet, DenseNet-169, and VGG16—applied to the binary classification of Tuberculosis (TB) using chest X-ray images.

Classifies images on fruits using the VGG16 model

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Automated system for classifying chest X-ray abnormalities and generating radiologist-style reports using ResNet18, ResNet50, VGG16 for image classification and LLaMA-3.2-11B-Vision-Instruct for text generation.

Image classification using XGBOOST by extracting features using VGG16 ImageNet. This project explains the process of using XGBOOST for image classification using pre-trained weights (VGG16) as feature extractors.

The application demonstrates the advantage of using transfer learning for smaller datasets. Food-11 is a small dataset with 11K images for 11 categories of food. It uses features learnt by already trained VGG16 on large Imagenet data to train a classifier for Food Classification Task..

Transfer learning-based breast cancer classification using ultrasound images. Includes ResNet-50, AlexNet, VGG16, and GoogLeNet with fine-tuning, feature extraction, and Bayesian optimization.

3-class MIAS breast cancer classification system using early fusion of features from fine-tuned VGG16, VGG19, ResNet50, and DenseNet121. Multiple image preprocessing, data augmentation, upsampling, and adjusted weighted loss for better model performance.

This repository explores the use of eXplainable AI (XAI) to interpret deep learning models in underwater SONAR image classification. We utilize transfer learning with CNN architectures like VGG16 and ResNet50, and apply LIME and SP-LIME for transparent model explanations.

The motivation is to use attention mechanisms that can help improve the accuracy and efficiency of object classification by focusing on the most relevant parts of the image rather than processing the entire image. The proposed attention-based CNN architecture was adopted and compared comprehensively with the existing networks like VGG16, etc.

This project utilizes deep learning techniques to detect pneumonia from chest X-ray images. It employs the VGG16 convolutional neural network, pre-trained on ImageNet, and fine-tuned for binary classification (normal vs. pneumonia). The model is trained on a dataset of chest X-ray images and achieves high accuracy in distinguishing pneumonia cases.

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Use Pytorch to replicate some of the image classification models which include AlexNet, SqueezeNet, DenseNet, VGG16, MobileNet, ResNet18 and GoogLeNet.

Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.

The Fruit and Vegetable Classification Project using Convolutional Neural Networks (CNN) is based on two popular models VGG16 and VGG19 in the TensorFlow framework. The goal of the project is to build an AI system capable of classifying images containing different fruits and vegetables.

Glaucoma Detection and Classification using Deep Learning Glaucoma is a condition of eye in which optic nerve is damaged due to abnormally high pressure in the eye. It is a chronic and irreversible disease. It is one of the leading cause of blindness across the globe in people over the age of 60. There is no cure for glaucoma, but early detection and medical treatment can prevent from disease progression. A goal of this project was to use deep learning architecture to build a model to detect and classify glaucoma by combining multiple deep features. Keras was used to build the model. We used publicly available database Drishti-GS1. Methodology: This project was divided into two parts: Glaucoma Detection First, ROI (Region of interest) which is an area where optic disc and cup are located in the center and blood vessels of the Glaucoma fundus images were extracted using U shape convolutional neural network and then cup to disc ratio was calculated to classify if the image was glaucomatous or normal. This Paper was used for ROI extraction and disc segmentation. Glaucoma Classification Cup to disc ratio was used for glaucoma classification. VGG16 CNN model was used to distinguish between glaucoma and non-glaucoma related images from fundus images. Glaucoma severity can also be classified from cup to disc ratio: Mild ( CDR >0.3 and <0.5) Moderate (CDR >=0.5 and <0.8) Severe (CDR >=0.8)

CalTech256 image classification by Transfer Learning from VGG16 pre-trained model

This repository demonstrates image classification using the ResNet50 model and feature extraction using the VGG16 model, both pre-trained on ImageNet. The code covers loading images, preprocessing, class prediction decoding with ResNet50, and extracting feature representations with VGG16.

This project implements an advanced image classification pipeline leveraging multiple pre-trained deep learning models (VGG16, VGG19, MobileNet, and Xception) combined with attention mechanisms to enhance feature extraction and classification accuracy.