  Outdoor scene parsing models are often trained on ideal datasets and produce
quality results. However, this leads to a discrepancy when applied to the real
world. The quality of scene parsing, particularly sky classification, decreases
in night time images, images involving varying weather conditions, and scene
changes due to seasonal weather. This project focuses on approaching these
challenges by using a state-of-the-art model in conjunction with a non-ideal
dataset: SkyFinder and a subset from SUN database with Sky object. We focus
specifically on sky segmentation, the task of determining sky and not-sky
pixels, and improving upon an existing state-of-the-art model: RefineNet. As a
result of our efforts, we have seen an improvement of 10-15% in the average MCR
compared to the prior methods on SkyFinder dataset. We have also improved from
an off-the shelf-model in terms of average mIOU by nearly 35%. Further, we
analyze our trained models on images w.r.t two aspects: times of day and
weather, and find that, in spite of facing same challenges as prior methods,
our trained models significantly outperform them.
