multiclass-image-classification
There are 3 repositories under multiclass-image-classification topic.
vijayg15 / Keras-MultiClass-Image-Classification
Multiclass image classification using Convolutional Neural Network
Language:Jupyter Notebook 73MuhammedBuyukkinaci / TensorFlow-Multiclass-Image-Classification-using-CNN-s
Balanced Multiclass Image Classification with TensorFlow on Python.
Language:Python 69France1 / unet-multiclass-pytorch
Multiclass semantic segmentation using U-Net architecture combined with strong image augmentation
Language:Jupyter Notebook 60teja0508 / BCS_Body_Condition_Score_Cattle_Prediction
body-condition-score_cattle prediction.
Language:Jupyter Notebook 8VishalShah1999 / MultiClass-Image-Classification
This will help you to classify images into Multiple Classes using Keras and CNN
Language:Python 8krunalvaghani / image_classification
Binary or multi-class image classification using VGG16
Language:Python 7deepankarvarma / Handwriting-Recognition--OpenCV--Keras-and-TensorFLow
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.
Language:Python 5Manohara-Ai / Hand_Written_Digit_Recognizer
Building a CNN to identify hand written digits
Language:Python 4This repository is containing my Jupyter files.
Language:Jupyter Notebook 4neelkantnewra / Multiclass-disease-classification-using-modified-CNN
This repository contains models for Multi-class disease detection using Chest X ray. A detail analysis of our approach is mentioned.
Language:Python 4IIsameerII / Handwritten-Digit-Recognition-from-MNIST-Dataset
This project uses TinyVGG and Streamlit to classify handwritten digits.
Language:Python 3kaustubhbhavsar / animals-10-classification
Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.
Language:Jupyter Notebook 3AtulJoshi1 / image-classification
Multi-class classification by Deep Learning approach on image data.
Language:Jupyter Notebook 2This 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.
Language:Python 2Otatoess / HAM-skincancer
This repository contains a deep learning model for skin cancer classification using the InceptionV3 architecture. The model was trained on the HAM10000 dataset and is designed with computational efficiency in mind. It was developed to be able to run on a CPU.
Language:Python 2RajkumarGalaxy / birds
Photographs of Birds for Multi-target Images Classification
SubhangiSati / Identification-of-Gemstone-using-MobileNetV2-and-transfer-learning
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
Language:Jupyter Notebook 2thepankj / Image-Classification-Transfer-Learning-Heroku
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.
Language:Jupyter Notebook 2AjNavneet / CNN-MultiClass-Classification-PyTorch
PyTorch implementation of CNN model for multi-class classification.
Language:Jupyter Notebook 1anik475 / Vission-Transformer-for-Image-Segemntion-using-UNET-R
Implementation of V architecture with Vission Transformer for Image Segemntion Task
Language:Jupyter Notebook 1This 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.
Language:Python 1EMCL-Research-ITSecLab / ssci23-xai-network-traffic
SSCI23 Explainable AI in Network Traffic Classification
Language:Python 1HansakaDilshanJayawardana / MLAssignment
SLIIT 4th Year 2nd Semester Machine Learning Project
Language:Jupyter Notebook 1jayakumarpujar / Image-Classifier
Binary or Multi Classifier to classify images by using Deep learning Architecture.
Language:Python 1m3mentomor1 / Pneumonia_Detection_with_Lightweight-CNN-Models
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.
Language:Jupyter Notebook 1nadyanvl / COVID-Multiclass-App
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.
Language:Jupyter Notebook 1OldBonhart / iWildCam-2019
Kaggle competition - "iWildCam 2019"
Language:Jupyter Notebook 1rainaa0277 / Fashion-MNIST-Classifier-using-ANN
Fashion training set consist of 70,000 images divided into 60,000 training and 10,000 testing samples. Dataset samples consists of 28x28 grayscale image associated with a label from 10 calsses.
Language:Jupyter Notebook 1- Final_Year_Project-Snakes-Things-
rsachintha / Final_Year_Project-Snakes-Things-
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.
Language:Jupyter Notebook 1 - Obstacle_Detection_on_Lunar_Surface_using_U-Net
ShrirangKanade / Obstacle_Detection_on_Lunar_Surface_using_U-Net
This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.
Language:Jupyter Notebook 1 The Bird Species Classifier is an application built using a Convolutional Neural Network (CNN) to classify images of birds into one of 525 different species. It allows users to upload an image of a bird and receive a prediction of the bird species. Along with analysing the performance of various optimising algorithms.
Language:Jupyter Notebook 1SunilGolden / DermatoAI-ISIC2018
Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.
Language:Python 1venkatavamsi01 / CNN_VGG19_EfficientNet_Distraction_Detection
Driver Distraction Detection with CNN and Transfer Learning (VGG19, EfficientNet)
Language:Jupyter Notebook 1wittyicon29 / AppleAI--Apple-Disease-Detection-Using-CNN
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
Language:Jupyter Notebook 1SayamAlt / Cats-Dogs-and-Snakes-Image-Classification-using-CNNs
This project focuses on accurately classifying images of cats, dogs, and snakes using Convolutional Neural Networks (CNNs) in PyTorch. A custom CNN model was initially designed and trained, achieving strong classification performance. Additionally, state-of-the-art (SOTA) pre-trained image classification models such as AlexNet, ResNet50, and VGG16.
Language:Jupyter NotebookThis project focuses on classifying distinct weather images using Convolutional Neural Networks (CNN) built from scratch and fine-tuning various state-of-the-art (SOTA) pre-trained models like AlexNet, ResNet50, VGG16, and MobileNet v3 Large. The models are trained and evaluated on a custom weather dataset, leveraging PyTorch for deep learning.
Language:Jupyter Notebook