Omkar2118/datasets huggingface dataset:
PPMR Dataset
Full name: pediatric polymicrogyria MRI dataset

This dataset contains 23 patients in total. Within each patients MRI, not all images show evidence of PMG and some
slices are normal. Although the ratio between controls and patients is 3:1, the ratio between normal slices and anomaly
slices is around 5:1. Each patient’s brain includes around 150 scans on average.

Source: [A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI](https://arxiv.org/pdf/2211.12565.pdf)

Image Source: [https://arxiv.org/pdf/2211.12565.pdf](https://arxiv.org/pdf/2211.12565.pdf)

Introducing paper: A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI
ODDS
Full name: Outlier Detection DataSets (ODDS)

Outliers or anomalies are instances that do not conform to the norm of a dataset. Outlier detection is an important data mining problem that has been researched within diverse research areas and applications domains such as intrusion detection, fraud detection, unusual event detection, disease condition detection etc.

The exact notion of an outlier is different for different application domains. Hence, applying a technique developed for one domain to another is not straightforward. Moreover, availability of labeled data for training/validation of outlier detection methods is scarce and often noise contained in data tends to be similar to outliers, thus makes it difficult to distinguish them. Because of these challenges outlier detection is not an easy problem to solve. Furthermore, research on outlier detection has been held back by the lack of good benchmark datasets with ground truths. Existing benchmarks are typically either proprietary or else very artificial. Moreover, existing real-world outlier/anomaly detection datasets lack the availability of ground truth.

In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single platform for the research community. As such, we arrange the datasets based on their types into different tables in ODDS library.

The ODDS library is being actively developed since summer 2016 and is growing as a result of our research pursuits in outlier/anomaly mining and also to help the corresponding research community. Researchers are welcome to share their datasets with us to include in ODDS library by emailing srayana@cs.stonybrook.edu.
N-ImageNet
Full name: Large-Scale Dataset for Event-Based Object Recognition

The N-ImageNet dataset is an event-camera counterpart for the ImageNet dataset. The dataset is obtained by moving an event camera around a monitor displaying images from ImageNet. N-ImageNet contains approximately 1,300k training samples and 50k validation samples. In addition, the dataset also contains variants of the validation dataset recorded under a wide range of lighting or camera trajectories. Additional details about the dataset are explained in the paper available through this [link](https://arxiv.org/abs/2112.01041). Please cite this paper if you make use of the dataset.

Introducing paper: N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras
ProNCI

**ProNCI** consists of 22.5K proper noun compounds along with their free-form semantic interpretations. ProNCI is 60 times larger than prior noun compound datasets and also includes non-compositional examples.

Source: ["Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds](https://arxiv.org/pdf/2210.13039v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.13039v1.pdf](https://arxiv.org/pdf/2210.13039v1.pdf)

Introducing paper: "Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds