Image processing using conv-neural net Okay I am starting to understand the idea of neural networks but I still haven't been able to understand the use/benefits/implementation of convolutional neural networks especially for image processing.

What I don't understand is how to detect an object of different size.

Say we have a weights matrix of 50*50 and a face in the training set which is of 50*50 size . Now when you take the weighted sum of the matrix it will return a particular value say "X" . So now I understand the idea is to run the weights matrix over the entire image to get a lock over a region which gives a weighted sum of ~"X" now you have detected a face.

If the above mentioned understanding is true Then how is that an image of size 25*25 going to churn out a value anywhere clone to "X" . So the basic question is how to deal with relative sizes of the image .

If possible please suggest some good tutorials for the same .
First of all, CNNs are typically used with identical sized inputs. So, you resize your input to a constant size, no matter what.

Next, detecting an object and recognizing an object over many samples are different tasks. For object detection, you could be better of with R-CNNs. Its sources are also available and there is a Caffe tutorial for R-CNNs. Of course there are many other works on the same topic. For example, this one.