Week 2 : Reproducing the Results of Deeply Learned Attributes for Crowd Scene Understanding (Jing Shao et al)
Getting started with www_crowd 1- Downloaded all the data related to Jing Shao's aforementioned research . 2- Cloned the repository in GitHub. 3- Watched the video regarding the research. 4- Downloaded the .caffe models and prototype.txt given. Learning how to train my first network 5- As a tutorial, I read the official siamese network tutorial of caffe. terms that I should be familiar with (I will update what they mean once I learn): 1) Convolution The first layer in a CNN is always a Convolutional Layer . [1] In signal processing, to convolve means to create a function using two others. [2] In this case, convolution refers to the process of multiplication of the image with the kernel in a sliding manner . The kernel will be multiplicated with a portion of the image, which we call as the receptive field , and then will slide by one pixel to do it again. Output of the each multiplication will be only one integer. But this is not a standard matrix-multiplic