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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