Balanced Multiclass Image Classification with TensorFlow on Python.

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 contains models for Multi-class disease detection using Chest X ray. A detail analysis of our approach is mentioned.

Building a CNN to identify hand written digits

This project uses TinyVGG and Streamlit to classify handwritten digits.

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.

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.

Code for "A Novel Convolution Transformer-Based Network for Histopathology Image Classification Using Adaptive Convolution and Dynamic Attention"

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

Multiclass classification of images of cats, dogs and fish

SSCI23 Explainable AI in Network Traffic Classification

Binary or Multi Classifier to classify images by using Deep learning Architecture.

Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI

This 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.

Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.

Trained a multiclass classifer network using cifar100 dataset

Multi-class classification of footwear images using a convolutional neural network. Dataset and trained model available

Basic classification algorithm for the MNIST dataset that uses computer vision fundamentals including simple NNs