ResNet18 CIFAR-100 Open-Source Image Classification Model - Trained on CIFAR-100 with Nearly 80% Test Accuracy

Resnet18 Cifar100

Developed by edadaltocg

A small ResNet18 image classification model trained on the CIFAR-100 dataset, achieving a test accuracy of 79.26%.

Downloads 48

Release Time : 2/19/2023

Model Overview

This model is a lightweight ResNet18 architecture, specifically optimized for the CIFAR-100 dataset, suitable for image classification tasks.

Lightweight Architecture

Adopts the ResNet18 architecture with fewer parameters, making it suitable for resource-constrained environments.

High Accuracy

Achieves 79.26% accuracy on the CIFAR-100 test set.

Easy to Use

Can be quickly loaded and used via the timm library.

Image Classification

Multi-class Recognition

Education

Image Classification Teaching

Used as an example for teaching image classification in computer vision courses.

Helps students understand the basic principles of convolutional neural networks.

Research

Benchmark Model Comparison

Serves as a benchmark for comparing the performance of new models.

Provides a reliable baseline for comparison.

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