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Resnet50 Cifar10
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Resnet50 Cifar10
Developed by edadaltocg
A compact ResNet50 model trained on the CIFAR-10 dataset for image classification tasks, achieving a test accuracy of 94.65%.
Downloads 35
Release Time : 2/19/2023
Model Overview
This model is a lightweight image classification model based on the ResNet50 architecture, specifically optimized and trained for the CIFAR-10 dataset.
Model Features
High accuracy
Achieves 94.65% accuracy on the CIFAR-10 test set.
Lightweight
A ResNet50 variant optimized for the CIFAR-10 dataset with a compact model size.
Easy to use
Can be directly loaded as a pre-trained model via the timm library for convenient usage.
Model Capabilities
Image classification
Object recognition
Use Cases
Education & Research
Image classification teaching
Can be used for image classification demonstrations in computer vision courses.
Demonstrates the performance of ResNet architecture on small datasets
Industrial applications
Simple object recognition
Suitable for scenarios requiring quick recognition of 10 common object categories.
Achieves high-accuracy recognition for CIFAR-10 object categories
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