Omkar2118/datasets huggingface dataset:
CADB
Full name: Composition Assessment DataBase

To the best of our knowledge, there is no prior dataset specifically constructed for composition assessment. To support the research on this task, we build a dataset upon the existing AADB dataset, from which we collect a total of 9,958 real-world photos. We adopt a composition rating scale from 1 to 5, where a larger score indicates better composition. We make annotation guidelines for composition quality rating and train five individual raters who specialize in fine art. So for each image, we can obtain five composition scores ranging from 1 to 5. Given the subjective nature of human aesthetic activity, we perform sanity check and consistency analysis. We use 240 additional “sanity check” images during annotating to roughly verify the validness of our annotations. We also examine the consistency of composition ratings provided by five individual raters (see Supplementary). We average the composition scores as the ground-truth composition mean score for each image. More details about our CADB dataset will be elaborated in Supplementary. Besides, we observe the content bias in our CADB dataset, that is, there are some biased categories whose score distributions are concentrated in a very narrow interval. After removing 461 biased images, we split the remaining images into 8,547 training images and 950 test images, in which the test set is made less biased for better evaluation (see Supplementary).

*Cited from "Image Composition Assessment with Saliency-augmented Multi-pattern Pooling" Zhang, Bo and Niu, Li and Zhang, Liqing*

## Citation
@article{zhang2021image,
  title={Image Composition Assessment with Saliency-augmented Multi-pattern Pooling},
  author={Zhang, Bo and Niu, Li and Zhang, Liqing},
  journal={arXiv preprint arXiv:2104.03133},
  year={2021}
}

Introducing paper: Image Composition Assessment with Saliency-augmented Multi-pattern Pooling
RWanda Built-up Region Segmentation

We create Rwanda built-up regions dataset, a different and versatile in nature from previously available datasets. The varying structure size and formation, irregular patterns of construction, buildings in forests and deserts, and the existence of mud houses make it very challenging. A total of 787 satellite images of size 256 × 256 are collected at a high resolution (HR) of 1.193 meters per pixel and hand tagged for built-up region segmentation using an online tool Label-Box.

Introducing paper: Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery