Large-scale Scene UNderstanding Challenge
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
Image Source: https://www.yf.io/p/lsun
Variants: LSUN Roon Layout, LSUN Bedroom 128 x 128, LSUN Bedroom, LSUN, LSUN Horse 256 x 256, LSUN Churches 256 x 256, LSUN Cat 256 x 256, LSUN Car 512 x 384, LSUN Car 256 x 256, LSUN Bedroom 64 x 64, LSUN Bedroom 256 x 256
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Image Generation | BigGAN + gSR | Improving GANs for Long-Tailed Data … | 2022-08-21 |
Recent papers with results on this dataset: