数据集介绍
PASCAL Context数据集[1]由两部分组成:
- PASCAL VOC 2010 语义分割数据集;
- Context 标注。
PASCAL Context 总共有459个标注类别,包含 10103 张图像,其中 4998 用于训练集,5105 用于验证集。现在最广泛地用法是使用其中出现频率最高的 59 个类别最为语义标签,其余类别标记为背景即background。采用这一做法的论文有[1][2][3][4]等。
数据集制作
数据准备:
常用的59个类别索引如下:
[ 0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22,
23, 397, 25, 284, 158, 159, 416, 33, 162, 420, 454, 295, 296,
427, 44, 45, 46, 308, 59, 440, 445, 31, 232, 65, 354, 424,
68, 326, 72, 458, 34, 207, 80, 355, 85, 347, 220, 349, 360,
98, 187, 104, 105, 366, 189, 368, 113, 115]
可参考MXNetSeg对数据集进行预处理和加载。
参考文献
[1] R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fi- dler, R. Urtasun, and A. Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR 2014.
[2] L. Chen, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. TPAMI, 2018.
[3] G. Lin, A. Milan, C. Shen, and I. D. Reid. Refinenet: Multi- path refinement networks for high-resolution semantic seg- mentation. In CVPR, 2017.
[4] H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal. Context encoding for semantic segmentation. In CVPR, 2018.