Hello,
Suppose I am working with n RGB video frames with convolution kernels k x k. I can channelwise stack all the frames and use pytorch conv2d with kernel 3n x k x k or can simply use 3d convolutions with kernels n x 3 x k x k. So which should be used for highest accuracy? Theoretically, in both cases, the neural network should find either configuration comfortable as the parameter complexity and receptive field remains the same. Or is it that conv3d will be advantageous?
Thankyou very much.