SHREE

Building on top of the Faster RCNN architecture, we explored the task of predicting Salient regions in an image, and using this information to propose effective ROIs for detection and classification of objects. We modified the Region Proposal Network to include an Object Saliency metric, which allowed our model to learn a Saliency (Importance) Score of the regions, thereby providing an order to the eventually detected objects. Our results performed better than the existing MLNet model for ranking of the Salient objects. Since our architecture additionally penalized the model for producing false positives, our model outperformed the Faster RCNN model on mean Average Precision