Let's train vision transformers (ViT) for cifar 10 / cifar 100!

PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy FROM SCRATCH on CIFAR-10 with small number of parameters (= 6.3M, originally ViT-B has 86M).

Simple and easy to understand PyTorch implementation of Vision Transformer (ViT) from scratch, with detailed steps. Tested on common datasets like MNIST, CIFAR10, and more.

Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.

MAE for CIFAR，由于可用资源有限，我们仅在 cifar10 上测试模型。我们主要想重现这样的结果：使用 MAE 预训练 ViT 可以比直接使用标签进行监督学习训练获得更好的结果。这应该是自我监督学习比监督学习更有效的数据的证据。

Implementation of Conv-based and Vit-based networks designed for CIFAR.

Vision Transformer Pruning

Pytorch implementation of ViT on CIFAR-10.

Code for "CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction" on CIFAR-10/100.

A PyTorch implementation of Vision Transformers as described in: An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale trained on CIFAR-100 dataset

The Lightest Vision Transformer (ViT) trained from scratch out there to achieve 93.37 ± 0.07%” top-1 accuracy on CIFAR-10 within just 50 epochs.

ViT, gpt-2 ish re-implementation of vision transformer on MNIST and CIFAR-10

基于Keras实现ViT模型，通过预训练权重在cifar-10数据集进行fine tuning，对图像分类准确率测试。

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.

🔍 Comparing CNN vs Vision Transformer (ViT) on CIFAR-10 with GPU T4 | Deep Learning Insights & Results 📊

Vit_fine_tuning_cifar 是一个专注于计算机视觉领域的深度学习项目，其核心任务是利用强大的Vision Transformer (ViT) 模型，在经典的 CIFAR 数据集上进行微调（Fine-tuning），以实现高精度的图像分类。

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My custom ViT implementation and pre-trained ViT fine-tuning for CIFAR-10 dataset

PyTorch implementation of 'ViT' (Dosovitskiy et al., 2020) and training it on CIFAR-10 and CIFAR-100

training vit variants on cifar-10

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This is our EE6483 project which uses CNN,GoogleNets,and VIT on Cat vs Dogs and Cifar-10 Dataset

Client-level machine unlearning in federated learning using Diffusive Noise Injection (DNI). Our method removes target data contributions while preserving global model accuracy, evaluated on CNNs, ViTs, and benchmark datasets (CIFAR-10/100, MNIST, KVASIR).

vit cifar10 test with image size 32, 64

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Toy project implementing a small Vision Transformer and training it on CIFAR10

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.

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A class project for CIFAR-10 classification, evolving from CNN to ResNet and ViT.