Why do we need to resize images in CNN?
Since neural networks receive inputs of the same size, all images need to be resized to a fixed size before inputting them to the CNN [14]. However, large images not only occupy more space in the memory but also result in a larger neural network. Thus, increasing both the space and time complexity.
What is the importance of zero padding during the training of CNN?
Zero-Padding It’s a commonly used modification that allows the size of the input to be adjusted to our requirement. It is mostly used in designing the CNN layers when the dimensions of the input volume need to be preserved in the output volume.
How much memory does a single image take?
A single image takes 2400x2400x3x4 (3 channels and 4 bytes per pixel) which is ~70Mb, so you can hardly afford even a batch size 10. More realistically would be 5. Note that most of the memory will be taken by CNN parameters.
How big is the image size of CNN?
I am feeding my CNN with image size of 2048×2048. However I accidentaly forgot to change the size of the input: I was wondering why does the training still run even if the input images are larger than 32×32 pixels? Or does the CNN only recognizes part of the images? Thanks in advance.
How big should batch size be for CNN training?
In short, training will be slow. What batch size is reasonable to use? Here’s another problem. A single image takes 2400x2400x3x4 (3 channels and 4 bytes per pixel) which is ~70Mb, so you can hardly afford even a batch size 10. More realistically would be 5. Note that most of the memory will be taken by CNN parameters.
How to handle large images when training a neural network?
Thus your dataset size to be used in one iteration would reduce, thus would reduce the time required to train the Network. The exact batch size to be used is dependent on your distribution for training dataset and testing datatset, a more general use is 70-30.