This dataset contains images of clothing items while each image is labeled with 50 categories and annotated with 1000 attributes, bounding box and clothing landmarks in different poses. Four datasets are developed according to the DeepFashion dataset including Attribute Prediction, Consumer-to-shop Clothes Retrieval, In-shop Clothes Retrieval and Landmark Detection in which only Attribute Prediction is available without password requests. All the other datasets mentioned need to request for a password to unzip the data files and the access would be available after signing an Agreement. All these datasets are available for academic research and any commercial use is prohibited. More details about the datasets and download instructions can be found on http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html. Attribute Prediction dataset which contains 289,222 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/AttributePrediction.html.
In-shop-Clothes Retrieval dataset which contains 7,982 images can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html.
Consumer-to-shop Clothes dataset which contains 33,881 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/Consumer2ShopRetrieval.html.
Finally, Fashion Landmark Detection dataset which contains 123,016 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html.
Here is some information regarding the DeepFashion dataset:
Number of images in the dataset: More than 800,000 (60,000 images for the training set and 10,000 images for the test set)
Number of classes: 50 categories
If you use this dataset:
Make sure to cite the papers:
Z. Liu, P. Luo. S. Qiu, X. Wang, X. Tang, Powering Robust Clothes Recognition and Retrieval with Rich Anotations, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Z. Liu, S. Yan, P. Luo, X. Wang, X. Tang, Fashion Landmark Detection In The Wild, European Conference on Computer Vision (ECCV), 2016.
This dataset contains grayscale images for clothing generated by Zalando (https://jobs.zalando.com/tech/). The dataset is created to be a substitute for the original MNIST dataset for machine learning algorithms. This substitution seems necessary because achieving very high classification accuracies is easy by classical machine learning algorithms. Also, MNIST might have been overused. As a result, Fashion MNIST shares the same image size, training and test sizes and number of classes with original MNIST.
Here is some information regarding the Fashion MNIST dataset:
Number of images in the dataset: 70,000 (60,000 images for the training set and 10,000 images for the test set)
Image size: 28×28
Number of classes: 10 (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)
4 data files are available for download from https://github.com/zalandoresearch/fashion-mnist which contain training set images, training set labels, test set images and test set labels. Instead of downloading the dataset, you might clone the GitHub repository in the same address provided above. More details and loading commands can be found in the same GitHub repository. You might also be interested to take a look at the Kaggle page https://www.kaggle.com/zalando-research/fashionmnist/home.
If you use this dataset, make sure to cite the paper:
Han Xiao, Kashif Rasul, Roland Vollgraf. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017.