quality focal loss

Quality Focal Loss (QFL) is a variant of Focal Loss, a loss function used in deep learning for classification tasks. QFL is designed to address the class imbalance problem in training datasets, where some classes have significantly fewer samples than others, leading to the model having a bias towards the majority class.

QFL takes into account the quality of each sample in addition to its class label. The quality of a sample is a measure of its uncertainty or difficulty in classification, which is estimated using a confidence score or the model's softmax output. QFL assigns a higher weight to samples with low quality, which are typically misclassified or ambiguous, and a lower weight to samples with high quality, which are more confidently classified.

By incorporating sample quality into the loss function, QFL encourages the model to focus on the difficult samples and learn from them more effectively. This can result in improved accuracy, especially for classes with few samples.

QFL has been shown to outperform other state-of-the-art loss functions in various classification tasks, including object detection, semantic segmentation, and image classification.