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 70France1 / unet-multiclass-pytorch
Multiclass semantic segmentation using U-Net architecture combined with strong image augmentation
Language:Jupyter Notebook 58teja0508 / 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 4SubhangiSati / 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 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
SayamAlt / Flowers-Recognition
Successfully trained a deep learning model which can precisely predict the species of flowers based on their images.
Language:Jupyter Notebook 2The 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 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 2wittyicon29 / 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 2aevinj / ImageClassifier
Multiclass classification of images of cats, dogs and fish
Language:Python 1AjNavneet / 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 1FionaAmuda / Image-Classification
Multiclass classification using TensorFlow
Language:Jupyter Notebook 1HansakaDilshanJayawardana / MLAssignment
SLIIT 4th Year 2nd Semester Machine Learning Project
Language:Jupyter Notebook 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 1Nirmala-research / YOLOv7-XAI
Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI
early-detectionexplainable-aiexplainable-artificial-intelligenceimage-processinglime-explanationmelanoma-classificationmelanoma-detectionmulticlass-image-classificationshap-valuesskin-cancerskin-cancer-diagnosisskin-detectionimage-processing-yoloyolo-2025-new-version-latestyolo-object-detection-new-with-opencvyolo-object-detection-recent-with-opencvyolo-recent-with-opencvyolov7-xai-2025-object-detection-with-opencvLanguage:Python 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 SunilGolden / DermatoAI-ISIC2018
Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.
Language:Python 1tahirlee / RCG-Net-convolution-transformer-based-network-for-histopathology-image-classification
Code for "A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention"
Language:Python 1varmatilak22 / Food_Vision_App
Food Vision Pro is a Streamlit app built with TensorFlow and CNN architecture, leveraging EfficientNet for deep learning-based food image classification. The model is fine-tuned on the Food101 dataset using mixed precision training and data augmentation techniques to accurately identify food items. It also integrates the NutritionixAPI for fetching
Language:Python 1venkatavamsi01 / CNN_VGG19_EfficientNet_Distraction_Detection
Driver Distraction Detection with CNN and Transfer Learning (VGG19, EfficientNet)
Language:Jupyter Notebook 1