This is a project focused on identifying the presence of pneumonia in chest X-ray images. Each image can be classified into one of three categories: Bacterial Pneumonia, Viral Pneumonia, or Normal.

Computer vision deep learning project classifying 10 classes of food images with 90% accuracy, built with TensorFlow.

Computer vision deep learning project classifying 101 classes of food images with 80% accuracy, built with TensorFlow. Beats the baseline accuracy of 50.76% (Food101 paper) and 77.4% (DeepFood paper).

Trained a multiclass classifer network using cifar100 dataset

Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.

This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.

The project focuses on Identification of various Gemstone. The dataset consists of 87 classes.It shows the whole progress and model used to achieve final accuracy. You will gain knowledge of Computer Vision, The model used are CNN(Convolutional Neural Network), MobileNetV2 and VGGNet,The final model used was transfer learning with model MobileNetV2

This project uses TinyVGG and Streamlit to classify handwritten digits.

COVID-19 CT scan image classification using EfficientNetB2 with transfer learning and deployment using Streamlit. This project focuses on accurately classifying CT scan images into three categories: COVID-19, Healthy, and Others. Leveraging transfer learning on pretrained EfficientNetB2 models, the classification model achieves robust performance.

Employing advanced techniques, the project seamlessly integrates binary and multiclass classifiers for character classification. It offers a comprehensive analysis and adeptly addresses challenges in the realm of computer vision.This project was part of my uOttawa Master's in Computer Vision course (2023).

PyTorch implementation of CNN model for multi-class classification.

Sequential CNN model using tensorflow for multi-class classification on images.

Driver Distraction Detection with CNN and Transfer Learning (VGG19, EfficientNet)

Ths project explains how a bigener can use simple convolutional neural network (cnn) to classify car images

This repo contains code for conducting image classification on a dataset of fruit images. Two models are fit to the data; a simple sequential model which is akin to multiclass logistic regression, and a large pretrained CNN model (VGG16).

Apple disease detection using CNN is a GitHub repository that contains code for detecting diseases in apples using convolutional neural networks (CNNs). The repository uses a dataset of images of healthy and diseased apples to train the CNN model. The model is then used to classify new images of apples as healthy or diseased

Multiclass classification of images of cats, dogs and fish

This project was part of the MLx Cases section of the OxML 2023 summer school.