  We train a deep Convolutional Neural Network (CNN) from scratch for visual
aesthetic analysis in images and discuss techniques we adopt to improve the
accuracy. We avoid the prevalent best transfer learning approaches of using
pretrained weights to perform the task and train a model from scratch to get
accuracy of 78.7% on AVA2 Dataset close to the best models available (85.6%).
We further show that accuracy increases to 81.48% on increasing the training
set by incremental 10 percentile of entire AVA dataset showing our algorithm
gets better with more data.
