places
Datasets
All datasets matching “places”fsq-os-placesFoursquare OS Places is now a gated dataset on Hugging Face. Read more about why we are making this change here: https://medium.com/@foursquare/evolving-fsq-os-places-fa7a3f5197cd
Access FSQ OS Places
With Foursquare’s Open Source Places, you can access free data to accelerate geospatial innovation and insights. View the Places OS Data Schemas for a full list of available attributes.
Prerequisites
In order to access Foursquare's Open Source Places data, it is… See the full description on the dataset page: https://huggingface.co/datasets/foursquare/fsq-os-places.places365-256pxPlaces365-customi1-places365-challenge2016-tfrecordi1: 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.i1-places365-challenge2016-512-resolution-1m-tfrecordi1: 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 512×512 resolution. Concretely, we only retain raw images with a shorter… See the full description on the dataset page: https://huggingface.co/datasets/i1-datasets/i1-places365-challenge2016-512-resolution-1m-tfrecord.foursquare_places_100M
Foursquare OS Places 100M
Full Foursquare OS Places dump from https://opensource.foursquare.com/os-places/.
This is a single (geo-)parquet file based on the 81 individual parquet files from fused.io on https://source.coop/fused/fsq-os-places/2024-11-19/places.
As it's just 10Gb, it's fairly easy to handle as a single file and can easily be queried over modern technologies like httpfs.
Ways to query the file & visualize the results
If you just want to poke around in… See the full description on the dataset page: https://huggingface.co/datasets/do-me/foursquare_places_100M.