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LSUN (Large-scale Scene UNderstanding Challenge)

Introduced by Yu et al. in LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

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.

Source: Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs

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Benchmarks

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Task	Dataset Variant	Best Model
Image Generation	LSUN Bedroom 256 x 256	Diffusion ProjectedGAN
Image Generation	LSUN Churches 256 x 256	Projected GAN
Image Generation	LSUN Cat 256 x 256	Vision-aided GAN
Image Generation	LSUN Horse 256 x 256	Vision-aided GAN
Image Generation	LSUN Bedroom	StyleGAN
Image Generation	LSUN Car 512 x 384	Polarity-StyleGAN2
Image Generation	LSUN Bedroom 64 x 64	WGAN-GP + TT Update Rule
Image Generation	LSUN Bedroom 128 x 128	LadaGAN
Image Generation	LSUN Car 256 x 256	StyleGAN2
Image Generation	LSUN	BigGAN + gSR