|Description (include details on usage, files and paper references)||We contribute DeepFashion database, a large-scale clothes database, which has several appealing properties:
First, DeepFashion contains over 800,000 diverse fashion images ranging from well-posed shop images to unconstrained consumer photos.
Second, DeepFashion is annotated with rich information of clothing items. Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks.
Third, DeepFashion contains over 300,000 cross-pose/cross-domain image pairs.
Four benchmarks are developed using the DeepFashion database, including Attribute Prediction, Consumer-to-shop Clothes Retrieval, In-shop Clothes Retrieval, and Landmark Detection. The data and annotations of these benchmarks can be also employed as the training and test sets for the following computer vision tasks, such as Clothes Detection, Clothes Recognition, and Image Retrieval.
For more details of the benchmarks, please refer to the paper, DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations, CVPR 2016.
1. Category and Attribute Prediction Benchmark: [Download Page]
2. In-shop Clothes Retrieval Benchmark: [Download Page]
3. Consumer-to-shop Clothes Retrieval Benchmark: [Download Page]
4. Fashion Landmark Detection Benchmark: [Download Page]
If the above links are not accessible, you could download the dataset using Google Drive or Baidu Drive.