Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
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IoU losses are surrogates that directly optimize the Jaccard index. In semantic segmentation, IoU losses are shown to perform better with respect to the Jaccard index measure than pixel-wise losses such as the cross-entropy loss. The most notable IoU losses are the soft Jaccard loss and the Lovasz-Softmax loss. However, these losses are incompatible with soft labels which are ubiquitous in machine learning. In this paper, we propose Jaccard metric losses (JMLs), which are variants of the soft Jaccard loss, and are compatible with soft labels. With JMLs, we study two of the most popular use cases of soft labels: label smoothing and knowledge distillation. With a variety of architectures, our experiments show significant improvements over the cross-entropy loss on three semantic segmentation datasets (Cityscapes, PASCAL VOC and DeepGlobe Land), and our simple approach outperforms state-of-the-art knowledge distillation methods by a large margin. Our source code is available at: \href{https://github.com/zifuwanggg/JDML}{https://github.com/zifuwanggg/JDML}.
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