We present a line drawing restoration dataset which consists of 71 line drawing sketches by Leonardo Da Vinci.

This extends the previous Fashion144k dataset to have a much larger number of images, and uses the automatic curating approach proposed in our StyleNet paper to improve the quality of the images. To evaluate learning with noisy labels, we provide a selected subset of 66 noisy tags for all the images, and additionally provide a subset of manually curated tags for both training and evaluation.

We present the FashionStyle14 dataset which focuses on predicting the fashion style of images. The images focus on single individuals with fully visible poses. We provide expert-curated fashion style annotations for a total of 14 unique challenging classes that focus on modern Japanese fashion styles such as Gal, Natural, or Casual.

We present an automatically curated version of the Fashion144k dataset. In order to improve the quality of images, we annotated a small subset of images in which a single individual is roughly centered in the image as positive images. We then train a convolutional network in order to predict whether an image is positive or not, and use this network to automatically curate the rest of the dataset. Although this reduces the number of available images, the resulting images are of much higher quality and do not include product nor heavily distorted images.

We present the Fashion144k dataset, consisting of 144,169 user posts with images and their associated metadata, for predicting fashionability, that is, how fashionable the user and their outfit is in an image. We exploit the votes given to each post by different users to obtain measure of fashionability, and provide diverse metadata to perform analysis and predictions.

We present a dataset for the evaluation of deformation and illumination invariance of local image patch feature point descriptors. The dataset consists of 192 unique 640x480 grayscale images corresponding to 12 different objects. Points of interest are obtained by the Difference of Gaussians (DoG) detected and were manually matched in order to obtain a ground truth mapping.