We follow the ImageNet training protocol adapted for CIFAR-10 to ensure fair comparison with ViG. All models in the comparison are trained under identical conditions:

Same resolution (224×224)
Same training recipe
Same data augmentation
No pretrained weights

Available Checkpoints

best_model.pth - Best performing checkpoint (93.09% accuracy)
final_model.pth - Final model after all epochs
checkpoint_epoch_X.pth - Saved every 50 epochs

Usage

python

import torch
from torchvision import models

# Load model
model = models.resnet18(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, 10)

# Load trained weights
checkpoint = torch.load('best_model.pth')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

Citation

Training protocol adapted from:

ResNet paper (He et al., 2015)
Vision GNN paper (Han et al., 2022)

iamgarvit/resnet18-cifar10-224

ResNet-18 on CIFAR-10 @ 224×224 - Vision GNN Baseline

Model Description

Training Details

Results

Methodology

Available Checkpoints

Usage

Citation

iamgarvit/
resnet18-cifar10-224