Solve image classification problems from scratch with this Python toolkit.

Classification using advanced Convolution Neural Networks and the Intel Image dataset, featuring 6 classes of color pictures in 150x150 pixels resolution.

Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data. Here is a notebook of different augmentation techniques.

A Northern EU mushroom image classifier trained on a FGVCx dataset with fastai and ResNet34.

This project uses deep learning to classify flower images as "roses" or "daisies," employing data augmentation, ResNet-18 architecture, and hyperparameter tuning.

Developing a CNN-based brain tumor classification system using MRI for improved diagnostics

Vegetables object localization app using tensorflow.

Classify 10 problems using the image from Traffy fondue report.

CIFAR10 Dataset.

A food classifier model that will help you to enjoy various kinds of foods for the first time in life.

Train Your CNN model on any object without any need of your custom dataset by using webscraping

Using a Convolutional Neural Network to determine the label of a photo. Picture scraping via an API.

This project evaluates and compares the prediction performances of various state of art pre-trained image classification models in classifying 5 types of flowers.

This repository contains the files and information related to the second project done by Lauren Smith, Ann Sofo and Reese Quillian in the DS 4002 Project Course.

A machine learning model that can classify real and AI generated images

Classify different sports images to know the sport using Transfer Learning

Multiclass image classification project on Kaggle Gemstones dataset

The image classifier to recognize different breeds of dog. Dataset used contains 50 images of dogs. In Image Classifier Project Resnet18, Alexnet, VGG16 from torchvision.models pretrained models were used. It was loaded as a pre-trained network, in which input images are transformed and resized for better prediction.

Deep Learning Project

Develop ML models for image classification, then convert those models into the TF-Lite file format, which can be embedded on Android and iOS.