The DataFrame should store the data as numpy arrays.
If you call torch.tensor(np.array(...)), you’ll create a copy of the data, so that’s why I suggested to use torch.from_numpy to just point to the numpy array and use the same memory.
Did it still yield the OOM issue?
Regarding the ImageNet question, @smu226 answered it nicely