The Large-scale Scene Understanding (LSUN) challenge aims to provide a different benchmark for large-scale scene classification and understanding. The LSUN classification dataset contains 10 scene categories, such as dining room, bedroom, chicken, outdoor church, and so on. For training data, each category contains a huge number of images, ranging from around 120,000 to 3,000,000. The validation data includes 300 images, and the test data has 1000 images for each category.

Dataset

This dataset was created by Shamim Ahamed

Released under Data files © Original Authors

Contents

Dataset Card for LSUN (r) for OOD Detection

  Dataset Details





  Dataset Description

Original Dataset Authors: Limin Wang, Sheng Guo, Weilin Huang, Yuanjun Xiong, Yu Qiao OOD Split Authors: Shiyu Liang, Yixuan Li, R. Srikant Shared by: Eduardo Dadalto License: unknown

  Dataset Sources

Original Dataset Paper: http://arxiv.org/abs/1610.01119v2 First OOD Application Paper: http://arxiv.org/abs/1706.02690v5

  Direct Use

This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/detectors/lsun_r-ood.

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

LSUN Tower dataset is a subset of the LSUN dataset, with 708,264 images.

LSUN Dining Room dataset is a subset of the LSUN dataset, with 657,571 images.

LSUN Church dataset is a large-scale image dataset containing 30,000 images of churches.

A dataset for training the ArtScore model

This artifact bundles the five dataset archives used in our private federated clustering evaluation, corresponding to the real-world benchmarks, scaling experiments, ablation studies, and timing performance tests described in the paper. The real_datasets.tar.xz includes ten established clustering benchmarks drawn from UCI and the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7); scale_datasets.tar.xz contains the SynthNew family generated to assess scalability via the R clusterGeneration package ; ablate_datasets.tar.xz holds the AblateSynth sets varying cluster separation for ablation analysis also powered by clusterGeneration ; g2_datasets.tar.xz packages the G2 sets—Gaussian clusters of size 2048 across dimensions 2–1024 with two clusters each, collected from the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7) ; and timing_datasets.tar.xz includes the real s1 and lsun datasets alongside TimeSynth files (balanced synthetic clusters for timing), as per Mohassel et al.’s experimental framework .

Contents

1. real_datasets.tar.xz

Contains ten real-world benchmark datasets and formatted as one sample per line with space-separated features:

• iris.txt: 150 samples, 4 features, 3 classes; classic UCI Iris dataset for petal/sepal measurements.

• lsun.txt: 400 samples, 2 features, 3 clusters; two-dimensional variant of the LSUN dataset for clustering experiments .

• s1.txt: 5,000 samples, 2 features, 15 clusters; synthetic benchmark from Fränti’s S1 series.

• house.txt: 1,837 samples, 3 features, 3 clusters; housing data transformed for clustering tasks.

• adult.txt: 48,842 samples, 6 features, 3 clusters; UCI Census Income (“Adult”) dataset for income bracket prediction.

• wine.txt: 178 samples, 13 features, 3 cultivars; UCI Wine dataset with chemical analysis features.

• breast.txt: 569 samples, 9 features, 2 classes; Wisconsin Diagnostic Breast Cancer dataset.

• yeast.txt: 1,484 samples, 8 features, 10 localization sites; yeast protein localization data.

• mnist.txt: 10,000 samples, 784 features (28×28 pixels), 10 digit classes; MNIST handwritten digits.

• birch2.txt: (a random) 25,000/100,000 subset of samples, 2 features, 100 clusters; synthetic BIRCH2 dataset for high-cluster‐count evaluation .

2. scale_datasets.tar.xz

Holds the SynthNew_{k}_{d}_{s}.txt files for scaling experiments, where:

• $k \in \{2,4,8,16,32\}$ is the number of clusters,

• $d \in \{2,4,8,16,32,64,128,256,512\}$ is the dimensionality,

• $s \in \{1,2,3\}$ are different random seeds.

These are generated with the R clusterGeneration package with cluster sizes following a $1:2:...:k$ ratio. We incorporate a random number (in $[0, 100]$) of randomly sampled outliers and set the cluster separation degrees randomly in $[0.16, 0.26]$, spanning partially overlapping to separated clusters.

3. ablate_datasets.tar.xz

Contains the AblateSynth_{k}_{d}_{sep}.txt files for ablation studies, with:

• $k \in \{2,4,8,16\}$ clusters,

• $d \in \{2,4,8,16\}$ dimensions,

• $sep \in \{0.25, 0.5, 0.75\}$ controlling cluster separation degrees.

Also generated via clusterGeneration.

4. g2_datasets.tar.xz

Packages the G2 synthetic sets (g2-{dim}-{var}.txt) from the clustering-data benchmarks:

• $N=2048$ samples, $k=2$ Gaussian clusters,

• Dimensions $d \in \{1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024\}$

• Cluster overlap $var \in \{10, 20, 30, 40, 50, 60, 70, 80, 90, 100\}$

5. timing_datasets.tar.xz

Includes:

• s1.txt, lsun.txt: two real datasets for baseline timing.

• timesynth_{k}_{d}_{n}.txt: synthetic timing datasets with balanced cluster sizes C_{avg}=N/K, varying:

  • $k \in \{2,5\}$

  • $d \in \{2,5\}$

  • $N \in \{10000; 100000\}$

Generated similarly to the scaling sets, following Mohassel et al.’s timing experiment protocol .

Usage:

Unpack any archive with tar -xJf

This repository contains Unofficial access for SDIP-bicycles dataset

Official Repository, Project Page, Paper Self-Distilled Internet Photos (SDIP) is a multi-domain image dataset. The dataset consists of Self-Distilled Flickr (SD-Flickr) and *Self-Distilled LSUN (SD-LSUN) that were crawled from Flickr and LSUN dataset, respectively, and then curated using the method described in our Self-Distilled StyleGAN paper:

Self-Distilled StyleGAN: Towards Generation from Internet Photos Ron… See the full description on the dataset page: https://huggingface.co/datasets/rmokady/SDIP_bicycle.

The dataset used in the paper is LSUN bedroom and church-outdoor datasets (64×64).

Dataset Card for "unet-lsun-256"

More Information needed

MB "Lsun" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Dataset

This dataset was created by Marinela Adam

Contents

This repository contains Unofficial access for SDIP-elephants dataset

Official Repository, Project Page, Paper Self-Distilled Internet Photos (SDIP) is a multi-domain image dataset. The dataset consists of Self-Distilled Flickr (SD-Flickr) and *Self-Distilled LSUN (SD-LSUN) that were crawled from Flickr and LSUN dataset, respectively, and then curated using the method described in our Self-Distilled StyleGAN paper:

Self-Distilled StyleGAN: Towards Generation from Internet Photos… See the full description on the dataset page: https://huggingface.co/datasets/rmokady/SDIP_elephant.

This repository contains Unofficial access for SDIP-horses dataset

Official Repository, Project Page, Paper Self-Distilled Internet Photos (SDIP) is a multi-domain image dataset. The dataset consists of Self-Distilled Flickr (SD-Flickr) and *Self-Distilled LSUN (SD-LSUN) that were crawled from Flickr and LSUN dataset, respectively, and then curated using the method described in our Self-Distilled StyleGAN paper:

Self-Distilled StyleGAN: Towards Generation from Internet Photos Ron… See the full description on the dataset page: https://huggingface.co/datasets/rmokady/SDIP_horse.

The dataset used in the paper is a large dataset of images, including FFHQ, AFHQ-Cat, and LSUN-Church.

Dataset Card for "LSUN_bedroom_VQA_feliu"

Images are a subset of the LSUN-Bedroom dataset. More Information needed The attributes are binary answers to the following questions:

Is the floor visible in the image? Does the room have a window? Is there more than one bed? Does the room have natural light? Is there a carpet in the floor? Is it a classy room? Is it a hotel room? Is there at least one person in the room? Are there more than one people in the room? Is it an expensive room?… See the full description on the dataset page: https://huggingface.co/datasets/fformosa/LSUN_bedroom_VQA.

Dataset Card for "latent_lsun_church_256px"

This is derived from https://huggingface.co/datasets/tglcourse/lsun_church_train Each image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion Decoding from diffusers import AutoencoderKL from datasets import load_dataset from PIL import Image import numpy as np import torch

load the dataset

dataset = load_dataset('tglcourse/latent_lsun_church_256px')

Load… See the full description on the dataset page: https://huggingface.co/datasets/tglcourse/latent_lsun_church_256px.