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.

Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.

Photographs of Birds for Multi-target Images Classification

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.

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.

Kaggle competition - "iWildCam 2019"

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

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.