The problem Capture It Openly solves

Current subject removal techniques are very tedious because a user has to select a subject by drawing a mask manually, whereas our model creates the mask automatically and the user just have to select the subject he/she want to remove which is very efficient.

Challenges we ran into

1. We were not familiar with UNET architecture and CGANS ,so learning it was quite a tedious task
2. Tuning the hyper parameters to reduce loss during the learning process
3. Finding the correct dataset for the semantic segmenation and CGANS was the hardest part for us