This repository contains a PyTorch implementation for classifying the Oxford IIIT Pet Dataset using KNN and ResNet. The goal is to differentiate the results obtained using these two approaches.
Image segmentation on the oxford-iiit pet dataset using CNN's, GNN's and U-Nets
Training CNN based image classifiers to identify breeds of cats and dogs.
Training a Convolutional Neural Network to perform Image Segmentation on Oxford Pets Dataset
CNN for classifying the breed of cats and dogs in the Oxford-IIIT Pet Dataset
Semantic segmentation with fully convolutional networks on the Oxford IIIT pet dataset.
A dataset created using some images from the Oxford-IIIT Pet dataset, and then performing some data augmentation and processing
UNet and DeepLabV3 for instance segmentation. Starting from a rgb image of a pet the network should output a segmentation mask of the pet. Deep Learning and Generative Models' practical project developed in 2025.
Image segmentation task for IIIT-Oxford Pets Dataset. Develops and compares various segmentation architectures. Also introduces important utility functions.
Deep learning-based segmentation with U-Net architecture on Oxford-IIIT Pet dataset, leveraging VGG16 model.
Here I am presenting machine learning notebooks, which were used during various analysis, e.g. kaggle competitions: "Titanic - Machine Learning from Disaster", Spaceship Titanic", "House Prices - Advanced Regression Techniques" and "Digit Recognizer") and other assays.
Semantic Image Segmentation task solution for the Oxford-IIIT Pet dataset using U-Net neural network, Hyperband and Transfer Learning. AGH 2023/24
Exploring which cat breeds are most frequently misclassified as dogs by CNN models.
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