Getting very low accuracy on my CNN Model

Question

I have a built CNN model in pytorch and i am getting very less accuracy on it.

# model building
class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        #covolution layers
        self.conv=nn.Sequential(
            nn.Conv2d(3,16,3,1),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(16,32,3,1),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(32,64,3,1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        #fully connected layers
        self.fc=nn.Sequential(
            nn.Flatten(),
            nn.LazyLinear(64),
            nn.Linear(64,128),
            nn.Linear(128,6)
        )
    #forward pass
    def forward(self,x):
        x=self.fc(self.conv(x))
        return x
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model=CNN().to(device)
criterion=nn.CrossEntropyLoss()
optimizer=torch.optim.Adam(model.parameters(),lr=0.01)

#trainig Network
    epochs=10
    print('Training Started...')
    for i in range(epochs):
        tr_sample=0
        tr_correct=0
        for b,(image,label) in enumerate(train_loader):
            image=image.to(device)
            label=label.to(device)
            y_pred=model.forward(image)
            loss=criterion(y_pred,label)
            _,predicted=torch.max(y_pred,1)
            tr_sample+=label.size(0)
            tr_correct+=(predicted==label).sum().item()
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        trn_acc=100*(tr_correct/tr_sample)
    
        with torch.no_grad():
            n_samples=0
            n_correct=0
            for image,label in test_loader:
                image=image.to(device)
                label=label.to(device)
                y_eval=model.forward(image)
                _,predicted=torch.max(y_eval,1)
                n_samples+=label.size(0)
                n_correct+=(predicted==label).sum().item()
            acc=100*n_correct/n_samples
        print(f'Epoch: {i} | loss: {loss.item()} | train accuracy:{trn_acc:.4f} % | test accuracy: {acc:.4f} %')
    print('Training Finished !')

Here is the glimpse of accuracy which I am receiving.

How do i improve the accuracy of my model? I tried already tuning parameters but nothing seems to be working out. I am getting much better accuracy for same layers on tensorflow.