News

2016-08-08 More useful labels, annotations, and evaluation results for each benchmark will be released soon.

2016-08-08 Attribute Prediction Benchmark has been released. You do *not* need password to unzip the images. To access the other three benchmarks, please read download instruction below.

2016-07-29 If Dropbox are not accessable, please download the dataset using Google Drive or Baidu Drive.

2016-07-27 The benchmarks of “Fashion Landmark Detection” has been released. The download links of Dropbox are temporarily disabled because of too much traffic. Will fix soon.

2016-07-18 The “In-shop Clothes Retrieval Benchmark” and “Consumer-to-shop Clothes Retrieval Benchmark” have been released. Please read Download Instructions. The other benchmarks will be released soon.

Descriptions

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.

Please read Download Instructions below to access the dataset.

Benchmarks


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]


Category and Attribute Prediction Benchmark


2. In-shop Clothes Retrieval Benchmark: [Download Page]


In-shop Clothes Retrieval Benchmark


3. Consumer-to-shop Clothes Retrieval Benchmark: [Download Page]


Consumer-to-shop Clothes Retrieval Benchmark


4. Fashion Landmark Detection Benchmark: [Download Page]


Fashion Landmark Detection Benchmark



If the above links are not accessable, you could download the dataset using Google Drive or Baidu Drive.

Agreement

  • The DeepFashion is available for non-commercial research purposes only.
  • All images of the DeepFashion are obtained from the Internet which are not property of MMLAB, The Chinese University of Hong Kong. The MMLAB is not responsible for the content nor the meaning of these images.
  • You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  • You agree not to further copy, publish or distribute any portion of the DeepFashion. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
  • The MMLAB reserves the right to terminate your access to the DeepFashion at any time.

Download Instructions

  • Some image data are encrypted to prevent unauthorized access. Please download the DeepFashion dataset Release Agreement.
  • Read it carefully, complete and sign it appropriately. This is an example.
  • Please send the completed form to Ziwei Liu (lz013(at)ie.cuhk.edu.hk) and cc to Ping Luo (pluo(at)ie.cuhk.edu.hk) using institutional email address. The email Subject Title is "DeepFashion Agreement". We will verify your request and contact you with the passwords to unzip the image data.

Citation

Please cite the following papers if you use the dataset.
@inproceedings{liuLQWTcvpr16DeepFashion,
 author = {Ziwei Liu and Ping Luo and Shi Qiu and Xiaogang Wang and Xiaoou Tang},
 title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
 booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = June,
 year = {2016} 
 }
@inproceedings{liuYLWTeccv16FashionLandmark,
 author = {Ziwei Liu and Sijie Yan and Ping Luo and Xiaogang Wang and Xiaoou Tang},
 title = {Fashion Landmark Detection in the Wild},
 booktitle = {European Conference on Computer Vision (ECCV)},
 month = October,
 year = {2016} 
 }

Contact


Please contact Ziwei Liu for questions about the dataset.