Image classification of RGB images in a Convolutional Neural Network is usually done by feeding the CNN first layer with a Numpy array with dimensions: pixel array by the number of channels. Example: [256 pixels] x [256 pixels] x [3 channels].
What is the impact on performance if applying a different Numpy array in a different order: say [256 pixels] x [3 channels] x [256 pixels], assuming that the CNN first layer structure is changed?
- Does the CNN architecture of the sub-sequent layers need to be changed?
- Would the model prediction accuracy be impacted?
My use case is a bit different where I have multiple features in a 5 dimensional Numpy Array [X pixels],[Y pixels],[N size],[M size],[L size] and I'm concerned that the order of how the features are entered into the CNN will impact the model training.