The iMaterialist Fashion Attribute Dataset
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The iMaterialist Fashion Attribute Dataset. / Belongie, Serge; Guo, Sheng; Huang, Weilin; Zhang, Xiao; Srikhanta, Prasanna; Cui, Yin; Li, Yuan; Scott, Matthew R.; Adam, Hartwig.
arxiv.org, 2019.Research output: Working paper › Preprint › Research
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T1 - The iMaterialist Fashion Attribute Dataset
AU - Belongie, Serge
AU - Guo, Sheng
AU - Huang, Weilin
AU - Zhang, Xiao
AU - Srikhanta, Prasanna
AU - Cui, Yin
AU - Li, Yuan
AU - Scott, Matthew R.
AU - Adam, Hartwig
PY - 2019/6/13
Y1 - 2019/6/13
N2 - Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total. Each image is annotated by experts with multiple, high-quality fashion attributes. The result is the first known million-scale multi-label and fine-grained image dataset. We conduct extensive experiments and provide baseline results with modern deep Convolutional Neural Networks (CNNs). Additionally, we demonstrate models pre-trained on iFashion-Attribute achieve superior transfer learning performance on fashion related tasks compared with pre-training from ImageNet or other fashion datasets. Data is available at: this https URL
AB - Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total. Each image is annotated by experts with multiple, high-quality fashion attributes. The result is the first known million-scale multi-label and fine-grained image dataset. We conduct extensive experiments and provide baseline results with modern deep Convolutional Neural Networks (CNNs). Additionally, we demonstrate models pre-trained on iFashion-Attribute achieve superior transfer learning performance on fashion related tasks compared with pre-training from ImageNet or other fashion datasets. Data is available at: this https URL
UR - https://arxiv.org/abs/1906.05750
M3 - Preprint
BT - The iMaterialist Fashion Attribute Dataset
PB - arxiv.org
ER -
ID: 304511757