Balanced Multiclass Image Classification with TensorFlow on Python.

This will help you to classify images into Multiple Classes using Keras and CNN

Binary or multi-class image classification using VGG16

This repository contains models for Multi-class disease detection using Chest X ray. A detail analysis of our approach is mentioned.

This project uses TinyVGG and Streamlit to classify handwritten digits.

This repository contains Python code for a project that performs American Sign Language (ASL) detection using multiclass classification. It utilizes YOLO (You Only Look Once) and MobileNetSSD_deploy for object detection, achieving an accuracy of 91%. The code offers options to predict signs from both images and videos.

Binary or Multi Classifier to classify images by using Deep learning Architecture.

This repository contains Python code for handwritten recognition using OpenCV, Keras, TensorFlow, and the ResNet architecture. The project utilizes two datasets: the standard MNIST 0-9 dataset and the Kaggle A-Z dataset. The OCR model is trained using Keras and TensorFlow, while OpenCV is used for image pre-processing.

Trained a multiclass classifer network using cifar100 dataset

SSCI23 Explainable AI in Network Traffic Classification

Multi-class classification of footwear images using a convolutional neural network. Dataset and trained model available

Multiclass classification of images of cats, dogs and fish

This repository contains Python code for rice type detection using multiclass classification. The project leverages the MobileNetV2 architecture to classify six different types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset used for training and evaluation can be found on Kaggle and consists of categorized rice 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).

Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.