RetinaNet is a powerful object detection model that uses a focal loss function to address class imbalance during training. This one-stage detector is made up of a backbone network and two subnetworks that work together to detect objects in an image.
What is RetinaNet?
RetinaNet is an advanced object detection model that uses a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone network is responsible for computing a convolutional feature map over an entire input image, while the subnetworks perform convolutional object classification and bounding box regression. The two subnetworks feature a simple design specifically created for one-stage, dense detection.
How Does RetinaNet Work?
RetinaNet uses a focal loss function to address class imbalance during training. This loss function applies a modulating term to the cross entropy loss, focusing learning on hard negative examples. The focal loss dynamically scales the cross-entropy loss, where the scaling factor decays to zero as confidence in the correct class increases. This scaling factor automatically down-weights the contribution of easy examples during training and rapidly focuses the model on hard examples.
The Focal Loss adds a factor $(1 - p\_{t})^\gamma$ to the standard cross entropy criterion. By setting $\gamma>0$, the relative loss for well-classified examples ($p\_{t}>.5$) is reduced, putting more focus on hard, misclassified examples. This approach is important in one-stage detectors, as they must process a much larger set of candidate object locations regularly sampled across an image.
Comparison with Two-Stage Object Detectors
RetinaNet's focal loss function was developed to address class imbalance in one-stage object detectors, which differ from two-stage object detectors. In two-stage object detectors, class imbalance is addressed by a two-stage cascade and sampling heuristics. The proposal stage rapidly narrows down the number of candidate object locations to a small number, filtering out most background samples. In the second classification stage, sampling heuristics are performed to maintain a manageable balance between foreground and background.
RetinaNet's one-stage detector must process a much larger set of candidate object locations regularly sampled across an image. This is why the focal loss function is so important, as it can automatically down-weight easy examples during training and rapidly focus the model on hard examples.
RetinaNet is a powerful object detection model that addresses class imbalance during training using a focal loss function. This one-stage detector is made up of a backbone network and two subnetworks that work together to detect objects in an image. While two-stage object detectors use a sampling heuristic to address class imbalance, RetinaNet addresses the issue with its unique focal loss function.
Overall, RetinaNet has shown impressive results and is constantly being improved. As technology advances, we can expect to see even more advancements in object detection models like RetinaNet.
