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Vit Cifar100

Developed by Ahmed9275
An image classification model fine-tuned on the Cifar100 dataset based on Google's ViT base model, achieving an accuracy of 89.85%
Downloads 920
Release Time : 5/18/2022

Model Overview

This model is an image classification model based on the Vision Transformer (ViT) architecture, specifically fine-tuned and optimized for the Cifar100 dataset, suitable for general image classification tasks

Model Features

High accuracy
Achieves 89.85% classification accuracy on the Cifar100 test set
Based on ViT architecture
Utilizes the Vision Transformer architecture, processing images with self-attention mechanisms
Efficient fine-tuning
Requires only 4 training epochs to achieve good performance

Model Capabilities

Image classification
Multi-category recognition

Use Cases

Computer vision
General image classification
Classifies images from 100 categories in the Cifar100 dataset
89.85% test accuracy
Educational research
Can serve as a benchmark model for computer vision education
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