Welcome back to the Advanced Object Detection blog post series! In our previous posts, we had a thorough understanding of YOLOv1, YOLOv2, YOLO9000 & the SSD Multibox detector. All are State-of-the-Art detectors that outperform each other brilliantly. In this post, we shall talk about another one of them - RetinaNet. RetinaNet is quite different from the YOLOs & SSD in a few aspects, the main one being the loss function. The RetinaNet employs a Focal Loss function that focuses less on soft or easy negatives and focuses more on hard samples. This was the class imbalance problem observed in training an object detector. The architecture uses an FPN (Feature Pyramid Network) with ResNet as the backbone CNN outperforms the Faster R-CNN and won the Best Student Paper Award in ICCV (International Conference on Computer Vision) 2021.
Applied Singularity 