Multiclass image classification using Convolutional Neural Network
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Updated
Aug 11, 2024 - 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 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.
This repository is containing my Jupyter files.
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Multi-class classification by Deep Learning approach on image data.
Successfully trained a deep learning model which can precisely predict the species of flowers based on their images.
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
Photographs of Birds for Multi-target Images Classification
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.
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.
Code for "A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention"
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