zlab-princeton/i1-places365-challenge2016-tfrecord

i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu Princeton University [arXiv][code][model][project page] Overview To prepare the dataset for training, we store the image-caption pairs as TFRecords. This HuggingFace dataset contains the TFRecords corresponding to the places365-challenge2016 dataset at 256×256 resolution. It also serves as an example of what a dataset… See the full description on the dataset page: https://huggingface.co/datasets/zlab-princeton/i1-places365-challenge2016-tfrecord.