Update README.md

README.md

@@ -64,7 +64,7 @@ dataset_summary: '
 
 # Dataset Card for pidray
 
-<!-- Provide a quick summary of the dataset. -->
+PIDray is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.
 
 
 
@@ -101,130 +101,46 @@ session = fo.launch_app(dataset)
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+From _Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark_:
+Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. With an intensive amount of effort, our dataset contains 
+ categories of prohibited items in X-ray images with high-quality annotated segmentation masks and bounding boxes. To the best of our knowledge, it is the largest prohibited items detection dataset to date. Meanwhile, we design the selective dense attention network (SDANet) to construct a strong baseline, which consists of the dense attention module and the dependency refinement module. The dense attention module formed by the spatial and channel-wise dense attentions, is designed to learn the discriminative features to boost the performance. The dependency refinement module is used to exploit the dependencies of multi-scale features. Extensive experiments conducted on the collected PIDray dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items.
 
 
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Language(s) (NLP):** en
 - **License:** apache-2.0
+The images and the corresponding annotations in PIDray Dataset can be used ONLY for academic purposes, NOT for commercial purposes.
 
-### Dataset Sources [optional]
+Copyright © 2021 Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences
 
-<!-- Provide the basic links for the dataset. -->
+All rights reserved.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Dataset Sources
 
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+- **Repository:** https://github.com/bywang2018/security-dataset
+- **Paper [optional]:** https://arxiv.org/abs/2108.07020
 
-[More Information Needed]
 
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### 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. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+## Uses
 
-**APA:**
+This used for academic research on airport security screening machines and the detection of objects being scanned.
 
-[More Information Needed]
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+### Out-of-Scope Use
 
-[More Information Needed]
+Any non-academic work is out of scope and prohibited.
 
-## More Information [optional]
 
-[More Information Needed]
 
-## Dataset Card Authors [optional]
 
-[More Information Needed]
+## Citation 
 
-## Dataset Card Contact
+@inproceedings{wang2021towards,
+   title={Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark},
+   author={Wang, Boying and Zhang, Libo and Wen, Longyin and Liu, Xianglong and Wu, Yanjun},
+   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+   pages={5412--5421},
+   year={2021}
+}
 
-[More Information Needed]