Upload README.md with huggingface_hub

README.md

@@ -5,24 +5,9 @@ task_categories:
 - image-classification
 paperswithcode_id: isun
 pretty_name: iSUN
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 24514257.375
-    num_examples: 8925
-  download_size: 0
-  dataset_size: 24514257.375
 ---
 
-# Dataset Card for iSUN
+# Dataset Card for iSUN for OOD Detection
 
 <!-- Provide a quick summary of the dataset. -->
 
@@ -36,7 +21,8 @@ dataset_info:
 
 
 
-- **Authors:** Junting Pan, Xavier Giró-i-Nieto
+- **Original Dataset Authors**: Junting Pan, Xavier Giró-i-Nieto
+- **Authors:** Shiyu Liang, Yixuan Li, R. Srikant
 - **Shared by:** Eduardo Dadalto
 - **License:** unknown
 
@@ -44,7 +30,7 @@ dataset_info:
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Paper:** http://arxiv.org/abs/1507.01422v1
+- **Paper:** http://arxiv.org/abs/1706.02690v5
 
 
 ### Direct Use
@@ -64,7 +50,7 @@ This dataset is not annotated.
 
 <!-- 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.
+The goal in curating and sharing this dataset to the HuggingFace Hub is to accelerate research and promote reproducibility in generalized Out-of-Distribution (OOD) detection.
 
 ### Personal and Sensitive Information
 
@@ -86,22 +72,30 @@ Please check original paper for details on the dataset.
 **BibTeX:**
 
 ```bibtex
-@article{1507.01422v1,
+
+@software{detectors2023,
+author = {Dadalto, Eduardo},
+title = {Detectors: a Python Library for Generalized Out-Of-Distribution Detection},
+url = {https://github.com/edadaltocg/detectors},
+doi = {https://doi.org/10.5281/zenodo.7883596},
+month = {5},
+year = {2023}
+}
+@article{1706.02690v5,
+Author        = {Shiyu Liang and Yixuan Li and R. Srikant},
+Title         = {Enhancing The Reliability of Out-of-distribution Image Detection in
+  Neural Networks},
+Eprint        = {http://arxiv.org/abs/1706.02690v5},
+ArchivePrefix = {arXiv},
+Year          = {2017},
+Month         = {6},
+Note          = {ICLR 2018},
+Url           = {http://arxiv.org/abs/1706.02690v5}
+}@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