The model will only accept 4D tensor of the kind (batch_size, channel, size,size)
so it will take in 1x3x224x224 if you give it one image at a time, or 10x3x224x224 if you give it 10 images at a time (i.e. batch size is 10).
While training it makes no sense to give one image at a time as it will make training insanely slow. If you want to pass one image at a time during inference, you should pass it in as a 4D tensor as I mentioned. For that you can use the unsqueeze() to add a dimension.