Datasets:

rmokady
/

SDIP_horse

@@ -37,7 +37,7 @@ Self-Distilled Internet Photos (SDIP) is a multi-domain image dataset. The datas
 [StyleGAN’s](https://github.com/NVlabs/stylegan2-ada-pytorch) fascinating generative and editing abilities are limited to structurally aligned and well-curated datasets. It does not work well on raw datasets downloaded from the Internet. The SDIP domains presented here, which are StyleGAN-friendly, were automatically curated by our [method](https://arxiv.org/abs/2202.12211) from raw images collected from the Internet. The raw uncurated images in *Self-Distilled Flicker (SD-Flickr)* were first crawled from [Flickr](https://www.flickr.com/) using a simple keyword (e.g. 'dog' or 'elephant').
-The dataset in this page exhibits 4 domains: SD-Dogs (126K images), SD-Elephants (39K images), SD-Bicycles (96K images), and SD-Horses (162K images). Our curation process consists of a simple pre-processing step (off-the-shelf object detector to crop the main object and then rescale), followed by a sophisticated StyleGAN-friendly filtering step (which removes outlier images while maintaining dataset diversity). This results in a more coherent and clean dataset, which is suitable for training a StyleGAN2 generator (see more details in our [paper](https://arxiv.org/abs/2202.12211)).
 The data itself is saved in a json format: for SD-Flickr we provide urls of the original images and bounding boxes used for cropping; for SD-LSUN we provide image identifiers with the bounding boxes. In addition to the SDIP dataset, we also provide weights of pre-trained StyleGAN2 models trained using each image domain presented in the paper.

 [StyleGAN’s](https://github.com/NVlabs/stylegan2-ada-pytorch) fascinating generative and editing abilities are limited to structurally aligned and well-curated datasets. It does not work well on raw datasets downloaded from the Internet. The SDIP domains presented here, which are StyleGAN-friendly, were automatically curated by our [method](https://arxiv.org/abs/2202.12211) from raw images collected from the Internet. The raw uncurated images in *Self-Distilled Flicker (SD-Flickr)* were first crawled from [Flickr](https://www.flickr.com/) using a simple keyword (e.g. 'dog' or 'elephant').
+The dataset in this page exhibits 4 domains: **SD-Dogs (126K images) 1024X1024**, **SD-Elephants (39K images) 512X512**, **SD-Bicycles (96K images) 256X256**, and **SD-Horses (162K images) 256X256**. Our curation process consists of a simple pre-processing step (off-the-shelf object detector to crop the main object and then rescale), followed by a sophisticated StyleGAN-friendly filtering step (which removes outlier images while maintaining dataset diversity). This results in a more coherent and clean dataset, which is suitable for training a StyleGAN2 generator (see more details in our [paper](https://arxiv.org/abs/2202.12211)).
 The data itself is saved in a json format: for SD-Flickr we provide urls of the original images and bounding boxes used for cropping; for SD-LSUN we provide image identifiers with the bounding boxes. In addition to the SDIP dataset, we also provide weights of pre-trained StyleGAN2 models trained using each image domain presented in the paper.