Focal Loss for Dense Object Detection introduces a novel loss function to address class imbalance in one-stage detectors. The focal loss modifies the standard cross entropy loss by adding a factor (1 - p_t)^γ, which reduces the loss for well-classified examples and focuses training on hard, misclassified examples. This loss enables training highly accurate dense object detectors despite the presence of many easy background examples. The paper presents RetinaNet, a simple one-stage detector that achieves state-of-the-art accuracy and speed. RetinaNet uses a feature pyramid network and anchor boxes, and is trained with the focal loss. Experiments show that RetinaNet outperforms previous one-stage and two-stage detectors on the COCO benchmark. The focal loss is effective in handling class imbalance by down-weighting easy examples and focusing on hard examples. The paper also discusses variations of the focal loss and their effectiveness. The results demonstrate that the focal loss significantly improves the performance of one-stage detectors, allowing them to match or surpass the accuracy of two-stage detectors while maintaining faster speeds.Focal Loss for Dense Object Detection introduces a novel loss function to address class imbalance in one-stage detectors. The focal loss modifies the standard cross entropy loss by adding a factor (1 - p_t)^γ, which reduces the loss for well-classified examples and focuses training on hard, misclassified examples. This loss enables training highly accurate dense object detectors despite the presence of many easy background examples. The paper presents RetinaNet, a simple one-stage detector that achieves state-of-the-art accuracy and speed. RetinaNet uses a feature pyramid network and anchor boxes, and is trained with the focal loss. Experiments show that RetinaNet outperforms previous one-stage and two-stage detectors on the COCO benchmark. The focal loss is effective in handling class imbalance by down-weighting easy examples and focusing on hard examples. The paper also discusses variations of the focal loss and their effectiveness. The results demonstrate that the focal loss significantly improves the performance of one-stage detectors, allowing them to match or surpass the accuracy of two-stage detectors while maintaining faster speeds.