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Resnet34 Cifar10

Developed by edadaltocg
A compact ResNet34 image classification model trained on the CIFAR-10 dataset, achieving 95.4% test accuracy
Image Classification EnglishOpen Source License:MIT#High-accuracy CIFAR10 classification#Lightweight ResNet34#Fast image recognition
Downloads 18
Release Time : 2/19/2023

Model Overview

This model is a lightweight implementation of the ResNet34 architecture, specifically optimized for CIFAR-10 image classification tasks, suitable for 10-class object recognition scenarios.

Model Features

High accuracy
Achieves 95.4% classification accuracy on the CIFAR-10 test set
Lightweight architecture
Optimized implementation based on ResNet34, suitable for deployment in resource-constrained environments
Ready-to-use model
Provides pre-trained weights that can be directly loaded and used via the timm library

Model Capabilities

10-class image classification
Object recognition
Feature extraction

Use Cases

Education & Research
Computer vision teaching
Used for image classification case studies in deep learning courses
Helps students understand convolutional neural networks and transfer learning
Industrial applications
Simple object recognition
Suitable for rapid recognition of the 10 object categories included in CIFAR-10
Can be integrated into lightweight visual detection systems
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