The iMaterialist Fashion Attribute Dataset
This provides a valuable resource for researchers and practitioners in computer vision and fashion AI, addressing a specific data bottleneck in the domain.
The authors tackled the lack of large-scale, high-quality multi-label and fine-grained image datasets by introducing the iMaterialist Fashion Attribute dataset, which includes over one million fashion images annotated with 228 fine-grained attributes, and demonstrated that models pre-trained on it achieve superior transfer learning performance on fashion tasks compared to ImageNet or other datasets.
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: https://github.com/visipedia/imat_fashion_comp