Omkar2118/datasets huggingface dataset:
EHR-Rel

EHR-RelB is a benchmark dataset for biomedical concept relatedness, consisting of 3630 concept pairs sampled from electronic health records (EHRs). EHR-RelA is a smaller dataset of 111 concept pairs, which are mainly unrelated.

Source: [EHR-Rel](https://github.com/babylonhealth/EHR-Rel)

Introducing paper: Biomedical Concept Relatedness -- A large EHR-based benchmark
MLGESTURE DATASET

MlGesture is a dataset for hand gesture recognition tasks, recorded in a car with 5 different sensor types at two different viewpoints. The dataset contains over 1300 hand gesture videos from 24 participants and features 9 different hand gesture symbols. One sensor cluster with five different cameras is mounted in front of the driver in the center of the dashboard. A second sensor cluster is mounted on the ceiling looking straight down.

Source: [MLGESTURE DATASET](https://iiw.kuleuven.be/onderzoek/eavise/mlgesture/home)

Introducing paper: Low-latency hand gesture recognition with a low resolution thermal imager
NYT-H

NYT-H is a dataset for distantly-supervised relation extraction, in which DS-labelled training data is used and several annotators to label test data are hired. NYT-H can serve as a benchmark of distantly-supervised relation extraction.

Source: [Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction](https://www.aclweb.org/anthology/2020.coling-main.566.pdf)

Introducing paper: Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
CSAW-S

CSAW-S is a dataset of mammography images which includes expert annotations of tumors and non-expert annotations of breast anatomy and artifacts in the image.

Source: [CSAW-S](https://arxiv.org/pdf/2008.00807.pdf)

Introducing paper: Adding Seemingly Uninformative Labels Helps in Low Data Regimes
2D-3D Match Dataset

2D-3D Match Dataset is a new dataset of 2D-3D correspondences by leveraging the availability of several 3D datasets from RGB-D scans. Specifically, the data from SceneNN and 3DMatch are used. The training dataset consists of 110 RGB-D scans, of which 56 scenes are from SceneNN and 54 scenes are from 3DMatch. The 2D-3D correspondence data is generated as follows. Given a 3D point which is randomly sampled from a 3D point cloud, a set of 3D patches from different scanning views are extracted. To find a 2D-3D correspondence, for each 3D patch, its 3D position is re-projected into all RGB-D frames for which the point lies in the camera frustum, taking occlusion into account. The corresponding local 2D patches around the re-projected point are extracted. In total, around 1.4 millions 2D-3D correspondences are collected.

Source: [2D-3D Match Dataset](https://github.com/hkust-vgd/lcd)

Introducing paper: LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
FIGR-8

The FIGR-8 database is a dataset containing 17,375 classes of 1,548,256 images representing pictograms, ideograms, icons, emoticons or object or conception depictions. Its aim is to set a benchmark for Few-shot Image Generation tasks, albeit not being limited to it. Each image is represented by 192x192 pixels with grayscale value of 0-255. Classes are not balanced (they do not all contain the same number of elements), but they all do contain at the very least 8 images.

Source: [https://github.com/marcdemers/FIGR-8](https://github.com/marcdemers/FIGR-8)
Image Source: [https://github.com/marcdemers/FIGR-8](https://github.com/marcdemers/FIGR-8)

Introducing paper: FIGR: Few-shot Image Generation with Reptile