Resnet34 Cifar100
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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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