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README.md

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+---
+license: unknown
+size_categories: 1K<n<10K
+task_categories:
+- image-classification
+paperswithcode_id: https://paperswithcode.com/dataset/isun
+pretty_name: iSUN
+---
+
+# Dataset Card for iSUN
+
+<!-- Provide a quick summary of the dataset. -->
+
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+
+
+- **Authors:** Junting Pan, Xavier Giró-i-Nieto
+- **Shared by:** Eduardo Dadalto
+- **License:** unknown
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Paper:** http://arxiv.org/abs/1507.01422v1
+
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+This dataset is not annotated
+
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+The objective in curating and uploading this dataset to HuggingFace is to accelerate research on generalized Out-of-Distribution (OOD) detection.
+
+## Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+Please check original paper for details on the dataset.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Please check original paper for details on the dataset.
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```bibtex
+@article{1507.01422v1,
+Author        = {Junting Pan and Xavier Giró-i-Nieto},
+Title         = {End-to-end Convolutional Network for Saliency Prediction},
+Eprint        = {http://arxiv.org/abs/1507.01422v1},
+ArchivePrefix = {arXiv},
+PrimaryClass  = {cs.CV},
+Abstract      = {The prediction of saliency areas in images has been traditionally addressed
+with hand crafted features based on neuroscience principles. This paper however
+addresses the problem with a completely data-driven approach by training a
+convolutional network. The learning process is formulated as a minimization of
+a loss function that measures the Euclidean distance of the predicted saliency
+map with the provided ground truth. The recent publication of large datasets of
+saliency prediction has provided enough data to train a not very deep
+architecture which is both fast and accurate. The convolutional network in this
+paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction
+with a superior performance in all considered metrics.},
+Year          = {2015},
+Month         = {7},
+Note          = {Winner of the saliency prediction challenge in the Large-scale Scene
+  Understanding (LSUN) Challenge in the associated workshop of the IEEE
+  Conference on Computer Vision and Pattern Recognition (CVPR) 2015},
+Url           = {http://arxiv.org/abs/1507.01422v1}
+}
+```
+
+## Dataset Card Authors
+
+Eduardo Dadalto
+
+## Dataset Card Contact
+
+https://huggingface.co/edadaltocg