Multiclass image classification using Convolutional Neural Network

Balanced Multiclass Image Classification with TensorFlow on Python.

Multiclass semantic segmentation using U-Net architecture combined with strong image augmentation

body-condition-score_cattle prediction.

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

Binary or multi-class image classification using VGG16

This repository is containing my Jupyter files.

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.

Multi-class classification by Deep Learning approach on image data.

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.

Photographs of Birds for Multi-target Images Classification

Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.

A multiclass image classification project, used transfer learning to use pre-trained models such as InceptionNet to classify images of butterflies into one of 50 different species.

PyTorch implementation of CNN model for multi-class classification.

SLIIT 4th Year 2nd Semester Machine Learning Project

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

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

This is the project I did as a part of my final year research regarding Multiclass Image Classification. This system identifies snake species relevant to the user uploading an image. A convolutional Neural Network was used to implement the image classification model and deployed using Flask. The model gained more than 80% of accuracy.

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.