Pytorch ViT for Image classification on the CIFAR10 dataset

This repository contains an implementation of the Vision Transformer (ViT) from scratch using PyTorch. The model is applied to the CIFAR-10 dataset for image classification.

Image classification in JAX with ViT, resnet, cifar10, cifar100, imagenette, and imagenet

A complete implementation of Vision Transformer (ViT) for CIFAR-10 image classification using PyTorch. This project demonstrates how to build and train a ViT model from scratch on the CIFAR-10 dataset.

Image classification and generation for CIFAR10 using ViT

Image classification using vision transformers

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Trained ViT-like architecture type with convolutional patch embedding to perform classification tasks on CIFAR10 dataset.

Test repository for CIFAR10 classification using ViT

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This project implements and compares Vision Transformer (ViT), ResNet-18 (Transfer Learning), and a CNN-MLP Hybrid model for image classification on CIFAR-10. It includes a Flask-based web app where users can upload images, select a model, and view real-time classification results.

In this project, we have trained two models for the classification of CIFAR 10 dataset images. In one model, we have used the pre-trained transformer based coat_lite_small model and optimized its last layers. In the other model, we have used the pre-trained DenseNet201 convolutional model and optimized its last layers.

In this project a create a model for image classification using ViT, Transfer Learning from pretrained models and the CIFAR10 dataset.