Issue with ImageNet training set

Hello guys,
I am trying to run resnet18 on Imagenet and here is the code
and i am getting a validation accuracy over 82.09% but the expected accuracy is 69.76%.
Is there a issue in my code?

import deeplake
import torch
import torchvision
from torchvision import transforms
from tqdm import tqdm

# Load pre-trained ResNet-18 model
model = torchvision.models.resnet18(pretrained=True)  # Load the pre-trained weights
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.eval()  # Set the model to evaluation mode

correct = 0
total = 0

# Iterate over the test_loader for validation
with torch.no_grad():
    for x, y in tqdm(test_loader):
        x =
        y =

        y_pred = model(x)
        predicted_labels = y_pred.argmax(dim=1)

        correct += (predicted_labels == y).sum().item()
        total += y.size(0)  # Increment the total count by batch size

accuracy = correct / total
print("Validation Accuracy: {:.2%}".format(accuracy))