This repository contains some deep learning architecture for intel image classification dataset.

A novel architecture for enhancing image classification. Reference paper: https://arxiv.org/abs/2104.12294

My extensive work on Multiclass Image classification based on Intel image classification dataset from Kaggle and Implemented using Pytorch 🔦

This Data contains around 25k images of size 150-150 distributed under 6 categories This data was initially published on Intel to host an Image classification Challenge.

Multi-class image classification model trained on the Intel Image Classification dataset using CNN architectures. The project focuses on building, training, and evaluating a deep learning model for scene classification.

Building a classifier on intel image dataset using transfer learning techniques and ResNet model

A CNN Image Classifier Example/Exercise

This project aims to classify images from the Intel Image Classification dataset using two renowned deep learning models: AlexNet and ResNet18. The notebook alex-res-net-classification.ipynb encompasses the entire workflow, including data preprocessing, model training, evaluation, and inference.

90% accuracy resnet-like CNN from scratch for Intel Image Classification dataset WITHOUT transfer learning and with complex metrics.

A Comparative Study of the performance of CNN from scratch compared to a transfer learning approach (InceptionV3)

The notebook contents implementation of image classification using different CNN models: self-written & pretrained, using data Intel Image Classification.

A comprehensive deep learning project for classifying natural scene images into 6 categories using Custom CNN and Transfer Learning (EfficientNet).