The Study of Render Farm Image Classification Using Deep Neural Network

Rendering on the making of movie animation is a process of combining the imagination of animators and artistic creativity by turning the graphic display into millions of moving images. To perform the rendering process, the render farm is used, which is a combination of a group of high-performance computers (super-computers) commonly referred to as Computer Generated Imager. Throughout this time, the render artist spends a considerable plenty of time on doing image analysis from render farms manually. Reflecting on that, the study presented in this paper propose a deep learning method can separate images of the results of render farms more quickly and accurately. The technique proposed in this study is the classification of approved or revised images as the results of the render farm machine by using Deep Neural Network (DNN) technique. To verify the DNN that best fits the dataset, experiments were carried out on several layer depths and adjustment of epoch. In terms of treatment of the dataset, the experiment scenario selected was percentage and cross validation. The best performance in the experiment results is provided by layer depth configuration of 10, epoch value 100. The configuration provides the value of accuracy, precision and recall of 90%, 96% and 92% respectively.