Multiclass image classification using Convolutional Neural Network
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Updated
Sep 24, 2021 - Jupyter Notebook
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.
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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.
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.
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.
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