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Resnet34 Cifar100

Developed by edadaltocg
ResNet34 image classification model trained on the CIFAR-100 dataset, achieving a test accuracy of 79.78%.
Downloads 42
Release Time : 2/19/2023

Model Overview

This is an image classification model based on the ResNet34 architecture, specifically trained and optimized for the CIFAR-100 dataset.

Model Features

High Accuracy
Achieves 79.78% accuracy on the CIFAR-100 test set
Lightweight Architecture
Based on the ResNet34 architecture, relatively lightweight and efficient
Easy to Use
Can be quickly loaded and used via the timm library

Model Capabilities

Image Classification
Multi-class Recognition

Use Cases

Computer Vision
Object Classification
Classify 100 categories of objects in the CIFAR-100 dataset
79.78% test accuracy
Educational Research
Used as a benchmark model in computer vision teaching and research
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