A VIKTOR application that demonstrates how VIKTOR could be used to implement Machine Learning models to help with detecting objects within images.

This is a CNN project in Python that classifies images from the Cifar10 dataset. The model used is ResNet-50, which has been trained on the dataset and saved as "resnet.h5" file. A Streamlit app has been created to load the model and predict the class of an image uploaded by the user.

Animal Classification using Transfer Learning Model (MobileNetV2)

Image classification using user created SVM Classifier

Solve image classification problems from scratch with this Python toolkit.

A deep-learning traffic sign detection and recognition project, using convolutional neural networks and vision transformers, implemented with PyTorch.

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.

Use a Pre-trained Image Classifier to Identify Dog Breeds

Classify roads if clean or littered.

ResPic is an image classification project that utilizes the power of a pre-trained ResNet50 model for accurate and efficient image classification.

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.

Testing the Opencv.dnn() class for object classification.

resnet-simple is a Python3 library that provides a well-documented and easy to use implementation of ResNet (and ResNetv1.5), together with its most basic use case of image classification. Uses PyTorch as the base for implementation.

Traning Pytorch model from image data