This repository presents a novel hybrid deep learning architecture that combines the strengths of both ResNet and Vision Transformer (ViT) for state-of-the-art image classification tasks.

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural networkbased models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

Deep Learning models for image classification based on Convolutional Neural Networks with 92% accuracy (ResNet).

This repository implements deep learning models for three computer vision tasks: Image Classification, Object Detection, and Semantic Segmentation. It uses CNNs for classification, ResNet for detection, and DeepLabV3 for segmentation, showcasing end-to-end solutions using PyTorch.

DeepFashion Image Classification is a project for deep learning-based clothing recognition using the DeepFashion dataset. It showcases reproducible pipelines and modular models (CNNs, ResNet, ViT, and more) for robust category classification. Designed for learning and experimentation within the fashion tech space.

Scalable Deep Learning project: ResNet-50 Multi-Class Image Classification with Parallel Computing on the collected 7000 real-world images database

A Deep-Learning model for image classification of a set of 10 food items. It uses the ResNet-50 pre-trained on ImageNet database.

ResNet-50 is a convolutional neural network architecture commonly used in deep learning for various computer vision tasks, such as image classification and object detection. It is part of the ResNet (Residual Network) family of architectures, which was introduced by Kaiming in 2015

Successfully implemented deep learning models (ResNet-50, VGG16, InceptionResNetV2) for medical image classification using TensorFlow and Keras. Employed transfer learning with pre-trained models and optimized with Adam optimizer. Achieved above 98% accuracy, precision, recall, and F1 score for classifying mri images to 4 Alzheimer's disease stages

An image-based document classification system that uses deep learning models (EfficientNet, ResNet, Vision Transformers) to automatically classify financial documents into predefined categories like Report, Resume, Letter, and more. Built with FastAPI for API integration and PyTorch for model training.

This repository contains code and datasets for a thesis on soybean seed image classification using deep learning. It compares the performance of YOLOv8 and ResNet-50 in classifying five seed types: broken, intact, spotted, immature, and skin-damaged, while also evaluating the impact of color augmentation on model accuracy.

Image Classification for a City Dog Show Project Goal Improving your programming skills using Python In this project you will use a created image classifier to identify dog breeds. We ask you to focus on Python and not on the actual classifier (We will focus on building a classifier ourselves later in the program). Description: Your city is hosting a citywide dog show and you have volunteered to help the organizing committee with contestant registration. Every participant that registers must submit an image of their dog along with biographical information about their dog. The registration system tags the images based upon the biographical information. Some people are planning on registering pets that aren’t actual dogs. You need to use an already developed Python classifier to make sure the participants are dogs. Note, you DO NOT need to create the classifier. It will be provided to you. You will need to apply the Python tools you just learned to USE the classifier. Your Tasks: Using your Python skills, you will determine which image classification algorithm works the "best" on classifying images as "dogs" or "not dogs". Determine how well the "best" classification algorithm works on correctly identifying a dog's breed. If you are confused by the term image classifier look at it simply as a tool that has an input and an output. The Input is an image. The output determines what the image depicts. (for example: a dog). Be mindful of the fact that image classifiers do not always categorize the images correctly. (We will get to all those details much later on the program). Time how long each algorithm takes to solve the classification problem. With computational tasks, there is often a trade-off between accuracy and runtime. The more accurate an algorithm, the higher the likelihood that it will take more time to run and use more computational resources to run. For further clarifications, please check our FAQs here. Important Notes: For this image classification task you will be using an image classification application using a deep learning model called a convolutional neural network (often abbreviated as CNN). CNNs work particularly well for detecting features in images like colors, textures, and edges; then using these features to identify objects in the images. You'll use a CNN that has already learned the features from a giant dataset of 1.2 million images called ImageNet. There are different types of CNNs that have different structures (architectures) that work better or worse depending on your criteria. With this project you'll explore the three different architectures (AlexNet, VGG, and ResNet) and determine which is best for your application. We have provided you with a classifier function in classifier.py that will allow you to use these CNNs to classify your images. The test_classifier.py file contains an example program that demonstrates how to use the classifier function. For this project, you will be focusing on using your Python skills to complete these tasks using the classifier function; in the Neural Networks lesson you will be learning more about how these algorithms work. Remember that certain breeds of dog look very similar. The more images of two similar looking dog breeds that the algorithm has learned from, the more likely the algorithm will be able to distinguish between those two breeds. We have found the following breeds to look very similar: Great Pyrenees and Kuvasz, German Shepherd and Malinois, Beagle and Walker Hound, amongst others.

A robust pipeline for iris recognition under challenging conditions using the UBIRIS.v2 dataset. Features synthetic noise simulation, data augmentation, deep learning-based image enhancement, and ResNet-50-powered classification for 214 classes.

A deep learning-based approach for hyperspectral image classification using Fast-3D CNN and ResNet, enhanced by advanced feature selection and extraction techniques.

This repository contains implementations for face classification using three deep learning models: ResNet, DenseNet, and Xception. These models are applied to classify facial images into predefined categories, showcasing their comparative performance and accuracy. The project highlights best practices in model development, training, and evaluation.

ResNet with Dropout for CIFAR-100 Image Classification. Explore this IPython notebook for a resilient CNN. Trained with ResNet architecture and dropout for enhanced accuracy on CIFAR-100 images. Model weights included for immediate use or further exploration. Dive into deep learning for robust image classification

A Deep learning framework evaluating Vision Transformers, ResNet, and MobileNet for DR classification using a balanced dataset combining APTOS-2019, preprocessed DR images, and GAN-generated samples.

AI-powered diabetic retinopathy detection using ResNet-152 deep learning. Web app with Flask REST API, PyTorch, real-time retinal image classification, 5-stage severity detection. Medical imaging, computer vision, transfer learning, healthcare AI, ophthalmology screening tool.

A deep learning-based image classification project that classifies flower images using pre-trained models such as ResNet-50, VGG, InceptionV3, and MobileNet. Fine-tuning techniques are applied to optimize each model's performance.

Employing a fusion of UNet and ResNet architectures, the project endeavors to achieve multiclass semantic segmentation of sandstone images. Through deep learning techniques, it seeks to uncover microstructural features across various geological classifications.

This is my first AI application that performs image classification using deep learning. Trained on my private datasets with the ResNet-50 model, it accurately predicts various animal categories. Users can upload images and receive predictions along with confidence scores.

Underwater species classification using deep learning. Evaluates ResNet-50, ViT, and Swin Transformer on a 23-species dataset (13,711 images). Swin Transformer achieved state-of-the-art 92.06% accuracy. Implements transfer learning, focal loss, and statistical validation for marine biodiversity monitoring.

This repository presents the implementation and evaluation of a ResNet-101 based deep learning model for detecting malnutrition in children using image classification. The model was trained, validated, and tested to distinguish between malnourished and well-nourished children, achieving state-of-the-art accuracy compared to existing studies.

This project aims to bring out an in depth comparison among existing image classification models on German Traffic Sign benchmark (GTSRB) images. In this project famous machine learning algorithms like Logistic Regression, SVMs, MLPs etc. and deep learning models like ResNet and CNNs have been implemented. Apart from this we also employed ensemble learning algorithm like Random Forest to get clear comparsion based on evaluation metrics like ROC Curves, Precision, Accuracies etc. Transfer Learning has also been implemented by extracting features of CNN and using them in other models.

We used an image classifier to identify dog breeds. For this image classification task we used an image classification application using a deep learning model called a convolutional neural network (often abbreviated as CNN). CNNs work particularly well for detecting features in images like colors, textures, and edges; then using these features to identify objects in the images. I used a CNN that has already learned the features from a giant dataset of 1.2 million images called ImageNet. There are different types of CNNs that have different structures (architectures) that work better or worse depending on your criteria. With this project you'll explore the three different architectures (AlexNet, VGG, and ResNet).

End-to-end deep learning pipeline to classify cat vs. dog images using CNN, ResNet, and ResNet50V2 with advanced tuning, evaluation, and deployment-ready predictions.

Deep learning image classification with CNN baseline and ResNet-18 on CIFAR-10 (PyTorch).

Using Deep learning method including ResNet, Inception v3, mobileNet, EfficientNet and transfer-learning for food image classification

Deep learning framework for cancer classification using dual-branch ResNet on unstained bright-field (UBF) and virtual H&E (VHE) images.

This image classification task uses deep learning models, from basic MLP to unpretrained ResNet to pretrained ResNet, which is an overall process covering data collection to model training.