Original article was published by /u/xuzhang5788 on Deep Learning
How can I build a 3D voxel-CNN model that can cover the whole objects and its meshes are enough fine to represent the details?
I want to build a 3D CNN model to classify the objects. Because I am familiar with 2D image classification problems, so I want to use voxel to represent the object. The size of the object is constant. If I want to represent the object as detail as possible, I need to use a finer meshes. But too fine meshes will occupy a lot of memory that my computer can not handle it. Also it will take so long training time.
If I only can handle 20x20x20 model, I can prepare my dataset like this. First of all, I mesh my objects into 20x20x20 and generate one input dataset. Then I mesh my objects into 30x30x30, but only take 20x20x20 parts as my second input dataset. Then, maybe I can do more refining my meshes and generate more input datasets.
I am thinking to use the progressive learning technic which is used in image recognition to solve my problem. However, my problem is a kind of reverse situation that the progressive learning can deal with. I don’t know how to train my model progresively. Your advice is highly appreicated.